Computing in Civil Engineering 2015

In bridge engineering, besides the design in 3D space scale, the construction at the time scale is also an important aspect that will influence the performance of a bridge in use stage. Two aspects of work are contained in this paper. Firstly, based on a three-span composite bridge of continuous box beam (88m+156m+88m), a full-scale finite element model was constructed in the program Midas FEA. Specially, the box beam is consisted of corrugated steel webs and concrete flanges. More than 300 thousand elements were applied in this full-scale model, to exactly simulate the authentic construction, shrinkage and creep of concrete, effect of pre-stressed-strand, shear lag effect and slip effect. Thus, the full-scale model may indicate the real mechanical behavior of the whole structure in both construction and use stage. Secondly, focusing on the connection style, two types of connectors including headed stud and ‘perfobond strip’ were applied in the composite box beam. Furthermore, to reduce the gravity of self-load, some parts of the concrete box in central span ware replaced by steel plate. And the performance of these three programs both in construction and usage were compared to approach an optimization design. With the simulation of 4D full-scale finite element model, the results wear reasonable and exact. And the comparison research indicated that the program with stud connectors and partly replaced box beam had the best performance in key index. The 4D full-scale finite element analysis method can provide reference and experience for further study.

This paper presents an Agent Based Model (ABM) for resource constrained scheduling of construction projects. The model considers typical scheduling characteristics such as the four precedence relationships. It also allows for activities to be interrupted, if needed. Moreover, the model considers the impact of the finish quality of a given activity on its successors. An illustrative example was presented to demonstrate the performance of the proposed model. Overall, the model was successful in minimizing project durations by utilizing different priority rules. ABM concepts were proved to be applicable in resource constrained scheduling. The model provided added versatility to activity characteristics.

Understanding the discrepancies between the nominal and actual production rate of construction machinery is important in construction projects execution. There are many site factors affecting the actual production rate. This paper presents a case study to understand the discrepancies between the nominal and actual production rates of crawler-type dozer and different main parameters affecting this production. The machinery considered includes different models of Caterpillar and Komatsu crawler-type dozers. The nominal production rates were derived from the two manufacturers. The actual production rates data were from records of actual construction projects. Each machine model was individually considered at project site, and the site conditions were taken into account. Working condition, type of materials, and ground slope are three parameters considered and evaluated for each machine. The distribution of working experience of operators and age group of all models of machine are also provided. The results of statistical analyses on the data and parameters show a) the discrepancies between the actual and nominal hourly production, and b) the importance of each parameter. The results of this study can be useful in planning of machinery and be a great help to the project management team.

Offshore pipelines are vital infrastructure systems for oil and gas transportation. Statistics around the globe confirm that third-party threats, such as vessel anchoring, fishing, and offshore construction, contribute the most to offshore pipeline damages and are the number one cause of death, injury, and pollution. This research studies satellite imagery and its application in automating vessel detection for the purpose of offshore pipeline surveillance. Current methods of relying on high-resolution satellite images lead to a high implementation cost and less efficient image processing. This paper proposes a method of utilizing lower resolution satellite images for vessel detection in offshore pipeline safety zones. It applies a combination of cascade classifier and color segmentation method as well as a unique “color-coding” scheme to achieve an accurate and efficient satellite image processing procedure. The proposed method was tested on 150 Google Earth satellite images with an average detection rate of 94% for large and medium vessels and an average false alarm rate of 19%.

Registration of 3D point clouds is one possible way to compare the as-built and the as-designed status of construction components. Building information models (BIM) contain detailed information about the as-designed state, particularly 3D drawings of construction components. On the other hand, using automated and accurate data acquisition methods such as laser scanning provide reliable and robust information about the as-built status of construction components. Registration therefore makes it possible to automatically compare the designed and built states in order to appropriately plan forward and generate the corrective actions required. This paper presents a new approach for reliably performing the registration with a required level of accuracy and automation within a substantially improved timeframe. Rather than performing the computationally intensive registration methods that may not work robustly for dense point clouds, the proposed framework employs the geometric skeleton of the construction components, which is extremely less dense and therefore computationally less costly for the processing step required. The method is experimentally tested for the components extruded along an axis, such as industrial assemblies (i.e. pipe spools and structural frames) for which a geometric skeleton represents the components abstractly. The registration of 3D point clouds is performed in a computationally less intensive manner, and the framework developed has the potential to be employed for (near) real-time assembly control, quality control and status assessment processes.

This paper introduces a new proximity alarming technology for roadway work zones. Roadway work zones are dynamic in nature and offer workers with limited work space, contributing to dangerous work environments for construction workers who are constructing and maintaining the infrastructure. Hazardous proximity situations can be encountered especially when the ground workers operate in close proximity to heavy construction equipment. Past research effort has been made in aiming at providing proximity sensing technologies for construction workers. These technologies, however, still have limitations that defer extensive deployment, which include accuracy, cost, required hardware, and ease of use. This study focuses on creating and evaluating a feasible technology that overcomes the drawbacks found in other technologies. Using Bluetooth sensing technology, a proximity detection and alert system was created. Experimental results demonstrate the created system’s ability to provide alerts to equipment operators and pedestrian workers at pre-calibrated distance in real-time. The proximity detection and alert device demonstrated its capabilities to provide, with an appropriate alarm, an additional layer of hazard avoidance to pedestrian workers in roadway work zone environments.

Household refrigerators are cyclic appliances whose operations comprise of disturbed and undisturbed cycles. While disturbed cycles are due to the interference of human activities, undisturbed cycles often occur late at nighttime and until early morning when there is less or no interference from building occupants. Better understanding of the operation cycles of refrigerators will not only help in improving the energy use of the appliance, but also develop the necessary constructs that will enable efficient operation via Smart Grid technologies. This paper uses an unsupervised learning, the k-means clustering method, to classify household refrigerator operation cycles for undisturbed periods. A typical household refrigerator was monitored for 11 weeks. Two parameters namely, maximum electricity consumption density and total electricity use for each refrigerator operation cycle were extracted from the data collected and were used for the classification of operation cycles. In order to analyze the actual working status (i.e., without human interference) of the refrigerator, data obtained between 1:00 a.m. to 6:59 a.m. were used for this study. Results show that the undisturbed refrigerator operation cycles can be distinctly classified into three groups namely, “general working cycle,” “auto-defrost working cycle,” and “auto-defrost affected working cycle.”

Ineffective tolerance analysis of prefabricated building components has a significant impact on the quality of accelerated construction projects. Emerging accelerated construction methods have adopted prefabrication techniques to improve construction productivity and balance workflows. The complex three-dimensional relationships among curved and deformed pipes and ductworks bring difficulties of analyzing how fabrication and installation errors of densely located pipes influence each other, and how such errors propagate throughout connected curvilinear components. This paper presents a computational framework that integrates 3D imagery data with Building Information Modeling to detect and analyze fit-up problems of curvilinear components. Using the detected deviations, an automated deviation classification algorithm derives the tolerance information of each individual component. This process generates a tolerance network describing how geometric variations of individual components influence each other. This tolerance network provides a quality control framework that enables adaptive redistribution of prefabrication and installation errors to resolve fit-up problems during accelerated construction.

Approaches to increase the energy efficiency of commercial buildings face the unique challenge of reconciling the discordant spatial scales at which centralized building systems, organizations, and individual occupants operate and utilize a building. Despite the importance of understanding spatial variability within a building, methods to spatially analyze data from wireless sensor networks (WSN) and building management systems (BMS) are limited. In this paper, we introduce the application of novel and highly scalable techniques from the field of discrete signal processing on graphs (DSPG) to the challenging problem of understanding the spatial variability of individual and organizational energy efficiency within commercial buildings. We collect and process individual-level electricity consumption data for two floors of a commercial building in Denver, Colorado and demonstrate the merits of our approach on the empirical dataset. Preliminary results indicate that occupants in different organizations exhibit largely different spatial patterns of energy efficiency compared to those in a single organization.

Pavement surface distress information is essential to pavement management. Assessment of surface permanent deformation is particularly important given pavement’s ubiquitous use and that permanent deformation significantly influences its integrity and performance. However, current assessment methods are expensive, labor-intensive, time-consuming, and potentially dangerous to inspectors. Digital aerial triangulation (DAT) and unmanned airborne systems (UAS) provide a new method for detecting and assessing permanent deformation that hold potential to overcome these limitations. Using hyperspatial resolution multispectral digital aerial photography (HRM-DAP) acquired from an UAS as input, DAT was employed to generate digital surface models (DSMs) of the pavement surface. Permanent deformation distress estimates extracted from these DSMs were compared to ground reference data collected using standard protocols via orthogonal regression analysis. The results show that the aerial triangulation of HRM-DAP provides consistent and reliable data for permanent deformation detection and assessment.

High definition construction cameras have been increasingly placed on construction sites to record jobsite activities into time-lapse videos. These videos have been used in several research studies to promote construction automation related to productivity analysis, site safety monitoring, etc. However, most videos tested in these studies were collected at daytime. It is not clear whether the time-lapse site videos collected at night are still useful, considering construction work might be performed both day and night for various reasons. The main objective of this paper is to investigate the effectiveness of recording jobsite activities with the time-lapse videos collected by construction cameras at night or other low ambient illuminant conditions through a case study. The construction site of the Romaine Complex project in Quebec is selected as the test bed. The cameras have been placed on the site to record the jobsite activities from day to night. All the collected site videos are classified based on their ambient illuminant conditions. Then, the videos under low ambient illuminations were tested with the object recognition techniques that have been commonly used in existing construction research studies. The recognition results indicated that the videos collected at night or other low ambient illuminant conditions are still useful and could be considered as an important source for site data sensing and analysis.

Comprehensive analysis of power consumption data for assorted home appliances or electrical devices can help identify a household’s electricity usage patterns as well as provide potential energy saving suggestions. Currently, modern metering techniques, such as smart meters, non-intrusive load monitoring, and Wi-Fi smart power outlets, can be used to collect the power consumption data in a timely and effective way. However, the data collected have not been analyzed in the context of the encompassing building, which certainly affects its energy use requirements such as heating and cooling. In addition, past research has showed that behavioral interventions are necessary to encourage a resident to save his or her utility bills, which requires disclosure of a household’s and the neighbors’ electricity usage patterns for comparison. Such the behavioral approaches have been successfully utilized by companies like Opower; nevertheless, they might invade one’s privacy if not properly managed. Thus, this research aimed at development of data fusion platforms with capability not only to integrate the power consumption data with the building data from a building information modeling (BIM) tool, but also to protect residents’ privacy concerns regarding their electricity usage patterns. Research work includes: (1) development of a tool to automatically transform a Revit file into Unity3D so that residents can see when and where the power is consumed and potential energy saving suggestions in a more interactive way; (2) development of a local platform, called local Real-Time Replay Platform (RTRP), to call a BIM-based energy analysis tool, Ecotect, to create the energy use baselines for residents for comparison; (3) development of a central platform, called central RTRP, to receive the aggregated power consumption data from several local RTRPs of different households so as to compile the neighborhood energy use baselines for all residents to compare with. Preliminary results are presented with research conclusions and future work discussions.

To ensure traffic safety, pavement conditions should be evaluated and distress (cracks, potholes, etc.) should be timely detected. In recent years, methods based on the analysis of digital images have been proposed to automatically detect pavement distress. However, most of these methods process the images offline and therefore require a large amount of data to be stored until actual processing. To enable real-time analysis of the images and to reduce the amount of stored data, a highly performant and computationally inexpensive implementation of a current analysis method is required. This paper presents a Graphics Processing Unit (GPU) implementation of an image analysis method for pavement distress detection. GPUs have been recently utilized for high-performance computing in diverse scientific fields. The pavement distress detection method presented in this paper is based on the wavelet transform. The implementation is carried out using the Open Computing Language (OpenCL). To evaluate the performance, the method was tested on 30 pavement images. The results show that a significant improvement in performance can be achieved by utilizing GPUs.

Learning curve analysis has been used for decades in construction industry to study the effect of experience on productivity, especially in repetitive jobs. Accurate and reliable estimate of time and cost are the benefits of performing a learning curve analysis. While learning curves exist in all levels of a project, detailed analysis of workers’ learning curve for short-term activities (that only span for a couple of minutes) require meticulous manual observation. This demand in manual effort overshadows the economic benefits from such analyses. Also, manual observations are time consuming, subjective and prone to human errors. This research outlines how data for automating such learning curve analyses can be gathered from a construction site. For this purpose, real-time location data using Global Positioning System (GPS) technology is collected from the workers as they perform their regular activities. Data acquired from GPS technology is used with occupancy grid analysis to calculate the amount of time spent by workers in specific area, which is used to demonstrate the spatio-temporal analysis of learning curve. A linear construction activity is presented as a case study. Results include automatic generation and visualization of learning curves for workers. The proposed method enables minute analysis of learning curves in activity level which can be directly associated to project level by following work breakdown structure. The method can be used in construction field for improving estimation, scheduling and training by project managers.

Technology independent elements such as the behavior of building occupants are significant factors responsible for energy consumption and associated CO₂ emissions of the buildings. Predicting building occupants’ energy-use has been identified as one of the most challenging processes due to high intra-class variability of occupant behavior. Previous research has benefited from classification techniques that serve to simplify the energy profiling by creating a codebook of energy-use patterns for each user. Nevertheless, the optimum level and type of energy-use behavior classification system (i.e., energy-use codebook) remains unknown. In this paper, we build on previous work and compare the relative effectiveness of various approaches to simplified representations, or codebooks, of building occupant energy-use behavior. We introduce the methodologies behind the construction of the codebooks and conclude that individually generated codebooks are a more accurate approach to model occupants’ energy-use compared to the globally generated codebooks. This contributes to the creation of more reliable energy-use profiling methods that can enhance the efficacy of automated energy efficiency programs.

According to US Bureau of Labor Statistics (BLS), in 2013 around three hundred deaths occurred in the US construction industry due to exposure to hazardous environment. A study of international safety regulations suggests that lack of oxygen and temperature extremes contribute to hazardous work environments particularly in confined spaces. Real-time monitoring of these confined work environments through wireless sensor technology is useful for assessing their thermal conditions. Moreover, Building Information Modeling (BIM) platform provides an opportunity to incorporate sensor data for improved visualization through new add-ins in BIM software. In an attempt to reduce Health and Safety (H&S) hazards, this work reports upon an integrated solution based on BIM and wireless sensor technology. A prototype system has been developed that: collects real-time temperature and oxygen data remotely from wireless sensors placed at confined spaces on a construction site; notifies H&S managers with information needed to make decisions for evacuation planning; and ultimately attempts to analyze sensor data to reduce emergency situations encountered by workers operating in confined environments. However, fusing the BIM data with sensor data streams will challenge the traditional approaches to data management due to huge volume of data. This work reports upon these challenges encountered in the prototype system.

In recent years, leading sustainability rating systems have extended the sustainability certification beyond buildings by introducing neighborhood level rating systems. The rationale is that sustainable buildings start with suitable site location and how well they integrate with their neighborhood. This paper discusses a Geographic Information System (GIS) based decision support system that aids owners, developers, and other stakeholders to analyze the project site location and its integration with the neighborhood in a sustainable manner. This decision support system uses four criteria to evaluate the project site location, a) connectivity to existing urban infrastructure; b) integration within current neighborhood; c) connectivity to transportation network; and d) environmental and agricultural land status. After reviewing literature on project site location in relation to neighborhood level sustainability rating systems, this paper discusses the results of preliminary test conducted using a neighborhood case study.

Mosques are distinguished from other types of buildings by having an intermittent operation schedule. They are partially or fully occupied five times a day and the maximum occupancy is expected to occur on Friday prayers. As buildings with intermittent occupancy may not perform the same thermally as typical commercial and residential facilities, thermal comfort conditions and perception of occupants have to be investigated. This paper presents the results of a study monitoring indoor environmental conditions of a mosque in order to assess thermal comfort conditions. A historic mosque, which is located in a hot and humid climatic region of Turkey, was selected as a test building and thermal comfort conditions were monitored during two Friday prayers in August and September. Indoor air temperature, relative humidity and air velocity were collected via data loggers. The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices were calculated and evaluated using the ASHRAE 55-2010 standard. In addition to this, a questionnaire based on Fanger’s seven-point scale was conducted to understand the thermal sensation and preference of occupants. A comparison is provided to highlight the difference between the calculated and perceived satisfaction of occupants.

Falls are the single most dangerous safety accident within the construction industry, representing 33% of all fatalities in construction. Numerous unrecognized near-miss falls exist behind every major fall accident. The detection of near-miss fall occurrence therefore helps the identification of fall-prone workers/tasks and invisible jobsite hazards and thereby can prevent fall accidents. This paper presents and evaluates the feasibility of a threshold-based approach for detecting the near-miss falls of construction iron-worker. Kinematic data of subjects are collected through an IMU sensor attached to the subjects’ sacrum; the subjects then perform walking on a steel beam structure. Fall-related features—sum vector magnitude (SVM), and normalized signal magnitude area (SMA)—are used to detect near-miss falls. Threshold values of these features are defined to achieve the best accuracy in near-miss fall detection based upon experiment data. According to selected threshold values, iron-workers’ near-miss falls were detected. The result of this research demonstrate the opportunity of utilizing SVM and SMA in documenting workers’ near-miss fall incidents in real-time.

Automated assessment of work-in-progress using large collections of site images and 4D BIM has potential to significantly improve the efficiency of construction project controls. Nevertheless, today’s manual procedures for taking site photos do not support the desired frequency or completeness for automated progress monitoring. While the usage of Unmanned Aerial Vehicles for acquisition of site images has gained popularity, their application for addressing issues associated with image-based progress monitoring and particularly leveraging 4D BIM for steering the data collection process has not been investigated before. By presenting examples from two case studies conducted on real-world construction projects, this paper suggests a framework for model-driven acquisition and analytics of progress images. In particular, the potential of spatial (geometry, appearance, and interconnectivity) and temporal information in 4D BIM for autonomous data acquisition and analytics that guarantees completeness and accuracy for both as-built modeling and monitoring work-in-progress at the schedule task-level is discussed.

Although transportation practitioners nowadays have an unprecedented level of access to massive information, there still exist substantial gaps in their ability to efficiently and reliably find the right information, at the right time, and for the task/decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach, which can capture and exploit the conceptualization of user needs, decision context, and content meanings to support the retrieval of information that is more relevant to decision making. The proposed IR approach includes three primary components: semantic annotation (SA), semantic query processing (SQP), and semantic document ranking (SDR). This paper focuses on SA for IR for supporting the Transportation Project Environmental Review (TPER) decision-making process. The paper proposes an epistemology-based SA algorithm for automatically annotating webpages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology is a semantic model for representing and reasoning about information and information retrieval in the TPER domain. In developing the proposed algorithm, a number of syntactic-based and semantic-based annotation methods/algorithms were developed and tested. For the syntactic-based algorithms, the effect of syntactic expansion and filtering was investigated. For the semantic-based algorithms, different semantic similarity calculation methods were evaluated. All the algorithms were tested on a testing data set of 1,328 webpages, which were collected from the FHWA Environmental Review Toolkit Website, and evaluated in terms of Mean Average Precision (MAP) and average precision. The final, proposed SA algorithm achieved 84.07% MAP and 90.67% average precision at top 50 documents, on the testing data.

One of the major goals of building information modeling is to support automated compliance checking (ACC). To support ACC, building design information needs to be extracted from building information models (BIMs) and transformed into a representation that would allow for automated reasoning about those design information in combination with information from regulatory documents. However, existing BIM information extraction (IE) efforts are limited in supporting complete automation of ACC. Complete automation of ACC requires (1) automating both the extraction of information from BIMs and the extraction of regulatory information from regulatory documents and (2) aligning the instances of information concepts and relations extracted from a BIM with those extracted from regulatory documents, in order to facilitate direct automated reasoning about both information for compliance assessment. To address this gap, this paper proposes an automated BIM IE method for extracting design information from industry foundation classes (IFC)-based BIMs into a semantic logic-based representation that is aligned with a matching semantic logic-based representation of regulatory information. The proposed BIM IE method utilizes semantic natural language processing (NLP) techniques and java standard data access interface (JSDAI) techniques to automatically extract project information from IFC-based BIMs and transform it into a logic format (logic facts) that is ready to be automatically checked against logic-represented regulatory rules (logic rules). The BIM IE method was tested on extracting design information from a Duplex Apartment BIM model. Compared to a manually developed gold standard, the testing results showed 100% precision and a short time of 15.02 seconds for processing 38,898 lines of data.

Given the critical role of specialty contractors in the construction industry, as the part who is involved in manual construction tasks and therefore is faced with hazardous attributes more than any others, finding innovative ways to enhance the safety performance of these types of contractors is promising. To address this challenge, the objective of this study is to utilize recently developed attribute-based risk assessment method and create a safety risk database, which then will be used to develop a safety-risk assessment tool (a mobile application) that can be operated by designers, project managers, and particularly specialty contractors to identify and assess risk of construction activities. The study focuses on electrical works, as this sector is one of the most hazardous trades in the construction industry (e.g. contact with electricity is the fourth main cause of fatalities in this industry). To build upon a reliable dataset, we obtained 323 accident reports from the OSHA IMIS database to create attribute-based safety risk models. Then, the attributes of construction objects and work tasks that cause injuries for electrical contractors are identified through a content analysis of injury reports and the relative risks associated with each attribute is quantified by recording the outcome of injuries. To predict potential outcome of injuries, a principal component analysis (PCA) and generalized linear model (GLM) conducted on the attribute based database and the predictive power of developed models is calculated using a rank probability skill score (RPSS). Ultimately, a mobile application has been generated to issue the findings.

Automated environmental regulatory compliance checking requires automated extraction of regulatory requirements/rules from environmental regulatory textual documents, such as energy conservation codes and environmental protection agency (EPA) regulations. Natural language processing (NLP) aims to enable computers to analyze and process natural text in a human-like manner. Information extraction (IE) is an application of NLP that aims to automatically extract specific information from text to support a specific computational task. In the proposed automated compliance checking (ACC) approach, after classifying the text for filtering out irrelevant regulatory provisions, pattern-matching-based IE techniques are used for extracting regulatory information, from the classified text, into certain predefined semantic patterns. In their previous work, the authors have proposed a semantic, rule-based methodology and algorithm for extracting information from building codes. This paper builds on the authors’ previous work in three main ways. First, the proposed IE algorithm is used in combination with text classification (TC) algorithms to enhance the efficiency (by avoiding unnecessary computational processing of irrelevant text) and performance (by avoiding potential noise and errors resulting from processing irrelevant text) of IE. Second, the IE algorithm is adapted to environmental regulatory text, which is different from building codes in terms of its syntactic and semantic features. Third, to enhance performance, a deeper (more detailed) ontology is used and a conceptual dependency structure is built to capture dependency information to reduce text ambiguities. The proposed IE algorithm was tested in extracting regulatory requirements from the 2012 International Energy Conservation Code, and the testing results showed 99.85% recall and 99.55% precision.

Spatial and spectral information provided by laser scanning or light detection and ranging (lidar) systems have been applied to various applications in the geospatial and civil engineering domains. In addition to precise 3D coordinates, most laser scanning systems also record intensity information, which have proven beneficial in point cloud segmentation and object extraction. In previous work, the authors demonstrated within controlled laboratory conditions that intensity values are an appropriate means for detecting wind-induced roof damage. However to be implemented successfully in real-world settings, the influence of scanning parameters such as the scanning angle of incidence should be investigated. The impact of these factors on the associated intensity values becomes increasingly important in terrestrial laser scanning when the point cloud data comprises registered scans that were captured from different locations to provide consistency. This paper presents examples of laboratory and field tests to investigate the influence of the scanning angle of incidence on lidar intensity measurement and classification for automated building wind damage detection. The tests indicated that captured lidar intensity information with a Leica C10 scanner can be used to classify and detect damaged areas on roofs when the scanning angle of incidence is less than 70 degrees.

Recently, the US Departments of Transportation have pro-actively looked into videotaping roadway assets. Using inspection vehicles equipped with high resolution cameras, accurate information on location and condition of high quantity and low cost roadway assets are being collected. While many efforts have focused on streamlining the data collection, the analysis is still manual and involves painstaking and subjective processes. Their high cost has also limited the scope of the visual assessments to critical roadways only. To address current limitations, this paper presents an automated method to detect, classify, and accurately localize traffic signs in 3D using existing visual data. Using a discriminative learning method based on Histograms of Oriented Gradients and Color, traffic signs are detected and classified into multiple categories. Then, a Structure from Motion procedure creates a 3D point cloud from the street level images, and triangulates the location of the detected signs in 3D. The experimental results show that the method reliably detects and localizes traffic signs and demonstrate a strong potential in improving assessments and lowering cost in practical applications.

Measurement and analysis of duty cycle of construction equipment is essential from the perspective of making decision with regards to controlling idle time and realizing productivity improvement. However, current monitoring techniques like Vehicle Health Monitoring Systems (VHMS) are either too expensive and/or are not compatible with outdated equipment fleets and equipment across different manufactures. To address these issues, we aim to develop a non-invasive technique of using a smart phone to measure the various activity modes (e.g. wheel base motion, cabin rotation and arm movement for excavator) and subsequently duty cycle of construction equipment. The smart phone is mounted inside the cabin of construction equipment to automatically capture engine vibration signatures in form of three-dimensional acceleration. Various time and frequency domain features are extracted from this raw data and are tested and classified into different equipment actions using machine learning algorithms from WEKA (Waikato Environment for Knowledge Analysis) data mining set. The classification accuracy on a random sample generated from various experiments on hydraulic excavator (CAT 330CL) was turned out to be between 72-86%. The average cycle time measurement accuracy based on predicted labels for equipment actions was around 88.5%. This result demonstrates the potential use of the proposed technique as an affordable system for automated and real time measurement of construction equipment duty cycle to facilitate detailed productivity analysis.

Sustainable building system design techniques aim to find an optimal balance between occupant comfort and the energy performance of HVAC systems. Design and implementation of effective heating ventilating and air conditioning (HVAC) controls is the key to achieve these optimal design conditions. Any anomalies in the functioning of a system component or a control system would result in occupant discomfort and/or energy wastage. While occupant discomfort can be directly sensed by occupants, measurement of waste in energy use would require additional sensing and analysis infrastructure. One way of identifying such a waste is to compare as-designed system requirements with the actual performance of the systems. This paper presents an analysis of an air handling unit (AHU) in a five story office building and provides the comparison results of design requirements against the sensor data corresponding to the AHU parameters. One year sensor data for the AHU parameters was analyzed to assess the correctness of the implementation of the design intent. The design intent was interpreted from the sequence of operations (SOOs) and confirmed with a commissioning engineer, who worked on the project. The design intent was then graphically represented as a pattern that the sensor data corresponding to the controls is expected to follow if it follows the design intent. Any deviation in the sensor data as compared to the expected operation pattern of the design intent indicated incorrect operation of the system with incorrectly implemented controls. The findings in this paper substantiate the need to formally define the sequence of operations and also point to the need to verify the implemented controls in a given project to detect any deviations from the actual design intent.

Micro-management refers to a management style whereby the managers closely observe and control the work details of subordinates or employees. Although micro-management generally has a negative connotation, the implications of adopting micro-management in construction projects remain unclear. This paper proposes the use of Agent Based Modeling (ABM) to investigate the impacts of micro-management on the efficiency, effectiveness, quality, and employee stress level in construction projects. A comprehensive simulation platform, Virtual Organizational Imitation for Construction Enterprises (VOICE), has been developed to simulate the proposal development of an EPC (Engineering, Procurement and Construction) project. The simulation results show that the micro-management has complex effects in the studied project, whereby decisional, behavioral, technical and institutional factors are interdependent. Micro-management in certain cases improves the efficiency and quality of proposal development. This paper contributes to the simulation studies in investigating social and behavioral problems in construction.

Laser scanners provide high-precision geometrical information, however, due to various scan errors, the generated point clouds often do not meet the data quality criteria set by project stakeholders for accurate data processing. Although, there exists several studies on identifying scan errors in literature, there is limited research on defining a data quality-driven scan plan for accurate detection of geometrical features. The authors propose a novel framework to integrate image-processing methods with point cloud processing techniques for defining a data quality-driven approach for scan planning. The framework includes the following steps: 1) capturing images of a target using a commercially available unmanned aerial vehicle (UAV) and generating a 3-D point cloud, 2) recognizing the project’s geometrical information using the point cloud, 3) extracting the features of interest (FOI) using the point cloud, 4) generating multiple scanning scenarios based on the data processing requirements and the extracted features, 5) identifying the best scan plan through iterative simulation of different scanning scenarios, and 6) validating the scan plan using real life data. The framework was evaluated using preliminary results of a case study. The results showed that the data quality requirements of the case study were met using the proposed framework.

In recent years, Structural Engineering practice has started adopting performance-based building design, a concept that produces a more resilient structural design as it accounts for the probabilistic nature of natural hazards such as earthquakes. Often times, a large number of dynamic building simulations have to be explored, producing results datasets that might typically be tens or hundreds of gigabytes. It is imperative to manage these datasets efficiently, to enable engineers run a larger number of simulations which helps produce more robust designs. Hierarchal Data Format (HDF) is a scientific data management library—to allow storing large amounts of data in hierarchal form on different platforms. SQLite is a relational database library based on the SQL language that can manage data efficiently on the local disk, without a server. In this study, the two libraries (HDF and SQLite) are explored for efficient dataset management, by comparing their efficiency using different performance metrics for storing and retrieving data in different scenarios. Two reading performance metrics were then considered, one based on data being read directly from disk (cold cache), and another for data being cached in memory (warm cache). Based on different building design simulations, performances of both libraries are compared for writing and reading with both warm and cold cache. According to the comparative study, it is concluded that the HDF library is faster in storing and retrieving big chunks of data and also required less storage space. Although the study was performed on structural simulation datasets, the conclusions are likely applicable to different kinds of datasets often encountered in many different engineering disciplines.

Sentiment analysis of social media has become a popular approach in many research areas. Most of the works were based on data collected from Twitter, which is a data source composed of text and geographic information. Many transportation researchers attempted to enrich and provide more accurate transport statistics through this approach. However, text data cannot fully provide a suitable explanatory of human’s behavior. In this paper, Instagram, which is a data source containing not only text and geographic information but also photographs, is utilized for the analysis. With computer vision techniques, there is the potential to better understand human’s travel behavior.

Occupancy behaviors have been recognized as a major source of uncertainty in assessment of energy performance in buildings. The limitations in collecting information regarding occupancy behaviors have hindered the ability to fully decode the emergent occupant behaviors affecting the energy performance of buildings. In this paper, we demonstrate our preliminary results related to a novel system for non-invasive tracking of occupancy behaviors. Our analysis will consist of the deployment of a system, consisting of a non-invasive sensor network, to capture the movements of occupants and their behaviors within a residence while recording the information related to the indoor temperature of the location. Utilizing small Infrared sensors, the building's occupant's movements are detected and the occupancy levels are recorded without having to uniquely identify individuals. The proposed system is demonstrated in a case study of a residential home. Data collected from the case study shows the capability of the proposed system in capturing occupancy behaviors using minimal information in a non-invasive and inexpensive fashion. The methodology used an serve as a basis for detecting behaviors that lead to energy waste in buildings. The system implemented has the ability to capture information required to integrate building performance and occupant behaviors towards the realization of smart buildings.

Laser scanning technologies have significantly improved the efficiency of spatial data collection to deliver the needed as-built information of job sites. However, many imageries contain unneeded dense data that waste time for data collection and processing, or missing data that are in need. Targeted laser scan data collection is pivotal to avoid such problems. For example, engineers could avoid densely sampling simple geometries (e.g. flat walls) for saving time to focus on edges and openings. Scan planning algorithms can produce data collection plans based on targeted objects marked by users. Unfortunately, manually defining objects and their needed level of detail (LOD) is impractical in dynamic environments, such as construction sites. This research proposes an approach that identifies visually complex regions through discontinuity analysis in rapidly captured sparse imageries for guiding the imaging planning. First, we fuse 3D point cloud data with sparse laser-scan data. A visual complexity analysis algorithm then detects locations in sparse imageries that contain discontinuous 2D and 3D patterns (e.g., color change) for identifying parts deserving detailed laser scan. A frequency analysis for each location would then estimate the LOD necessary for assessing each targeted region. Finally, a sensor-planning algorithm generates laser scan plans based on visually complex regions and LOD requirements.

Prefabrications has been gaining popularity in the construction industry over the past decade as it provides a safer and more sustainable operation as well as higher quality and cheaper construction components, due to its controlled conditions during fabrication, in-house quality assurance systems, and lower amount of waste. Despite the advantages of prefabrication methods, the current quality management processes, particularly in piping fabrication, are labour-intensive, time-consuming, expensive and rather inaccurate. This paper investigates automated solutions for improving the quality management system associated with the prefabrication of piping assemblies in the industrial sector of the construction industry. The scope of this research is the quality management processes conducted at the post-production stage of the fabrication process. The findings of this paper indicated that 3D laser scanning and photogrammetry techniques can both be successfully used in quality assurance systems for post-fabrication of pipe-spools. It was concluded that these methods exceeded the accuracy requirements of the current systems, while substantially improving the efficiencies of the quality assurance processes for prefabrication operations.

Recent advances in sensor technology have led to enhanced data acquisition capabilities in construction sites. A wealth of data are being collected from GPSequipped heavy vehicles for a wide range of monitoring, management, and analysis purposes. The availability of detailed GPS trajectory data has opened up new opportunities for modeling and simulation of real-world construction operations. One of the emerging areas in this regard is data-driven modeling and simulation, which is a modeling framework that attempts to automatically generate discrete-event simulation (DES) models based on a rich set of observed data as well as dynamically adapt the generated model to changes in data. Within the overall framework of automatically generating a DES simulation model for earthmoving operations, this paper focuses on developing methods to convert complex movement data collected from scrapers into a modeling element of activity-cycle diagram and activity scanning modeling paradigm-based DES system. Scraper changes travel routes at every cycle and its trip patterns (e.g., travel path and speed) are very difficult to generalize using a known parametric model (e.g., theoretical probability distribution), which in turn complicates the problem of automatic model generation. To deal with this issue, this paper proposes the use of relation between travel time and travel distance with regard to coefficient of variation measures, expressed in two separate distributions, to capture information needed to construct speed and path scenarios.

The ethos of building design offices has undergone radical transformation following the widespread adoption of digital technologies. Design calculations that were formally carried out by hand over a period of weeks and months are now completed in hours using highly sophisticated analysis and design packages. Likewise, the drawing board has been almost universally abandoned in favour of CAD systems and parametric 3D models. The purpose of this research is to identify the effect that digital technologies is having on operations of design offices. A series of semi-structured interviews were carried out with experienced practitioners (Consulting engineers and a steelwork sub-contractor) all of whom have witnessed the evolution of the industry to a digital environment. A subjective research philosophy was adopted using an inductive approach to analyse the data. Not surprisingly, it was found that the use of digital analysis, drawings and modelling suites have brought significant benefits to the industry. There are, however, many issues that are adversely impacting the industry; the over reliance on computer analysis by younger engineers was argued to be leading to a loss of understanding of the basic engineering principles in their designs. There is a tendency to ignore the historical philosophy of building design and it’s associated approximations with inexperienced engineers focusing on levels of accuracy more akin to manufacturing. With some modellers being trained in the use of the technology instead of the principles of engineering and construction, the quality of information being issued to constructors is often lacking.

Monitoring and analysis of energy consumption and performance of heating ventilation air conditioning (HVAC) systems in facilities can give insights about building behaviors for improving facility operations. However, due to the custom sensor infrastructure needed to monitor building performance parameters, additional costs would be incurred by owners to instrument facilities and their systems. Detailed analysis of building performance in highly sensed facilities can give insights about the behavior of similar facilities that have no budget to have such sensing infrastructure. This paper provides the analysis of energy consumption and HVAC performance data acquired in a minute interval in a highly sensed building through the comparison of several data mining algorithms, such weighted least square linear regression, random forest and K-nearest neighbor. The findings include the performance of such algorithms in identifying patterns and give insights about suitability of such algorithms in predicting the energy use and system performance in similar buildings with no sensor infrastructure.

The design and construction industry has the responsibility to provide the client with the product of highest value. This is a complex task. Project stakeholders typically propose discipline specific optimal solutions and have many conflicting interests. To provide an overall successful solution they need to correlate the discipline specific goals and constraints and resolve many conflicts. These are resolved through a negotiation process in which each party typically takes a self-interested position. An improved approach is to set discipline specific targets and develop solutions that meet these targets. This paper presents three project delivery scenarios and their impact on product value to the client: (1) no set target values; (2) designs that meet domain specific targets - progressive cost estimating Target Value Design (TVD), Sustainable Target Value (STV), and Life Cycle Cost (LCC); and (3) a preliminary integrated target value driven approach, linking TVD, STV, and LCC, to engage all stakeholders in an iterative process of exploration and decision making to provide the client with the highest product value. The AEC Global Teamwork course offered at Stanford with partners worldwide is used as a testbed.

The Construction Engineering and Management (CEM) workforce requires training on the systematic coordination the complex relationships of the interdependencies, interactions and constraints among engineering systems. The understanding of these relationships presents a significant challenge to master CEM practices in-situ. It is essential to fully develop the learning abilities to effectively incorporate the interdependencies and interactions with constraints to solve problems. Building upon our previous research, where we defined the individual’s spatial and temporal cognitive ability as the ability to couple and combine qualitative temporal and spatial abstractions, this research addresses the problem of mastering the complex relationships of the interdependencies, interactions and constraints. We use a layer of artificial visualizations to simulate the contexts where such spatial and temporal constraints are easily and rapidly observed. The superimposition of virtual objects on images is an Augmented Reality (AR) layer that serves as an instructional. The assumption is that the enhancement of spatial-temporal constraints enables learners to visualize context and hidden processes in situ.

One of the greatest challenges faced by humanity is the impact on climate due to human activity. Among the most prominent of these activities is the construction of buildings. As time and technology progress, the need for more efficient and environmentally-friendly processes and materials is gaining favor among the general population. In line with this, various government protocols have been implemented which have led to development of various software applications that assist in designing better buildings. However, the reach of these applications is limited owing to the cost and expertise needed to actually use these software applications. For instance, architects have multiple tools at their disposal for designing an energy efficient building. But most of these tools are costly to operate, time-consuming and require extensive and software-specific training. This has a greater impact in the initial stages of design, where the amount of data is very limited. The focus of this paper is to develop a plugin for SketchUp^®, which is the CAD design software most commonly used by architects, so as to give insight into building energy consumption during the initial design stages. The challenge is to develop a calculation methodology that is sufficiently accurate, requires very little data from the user, requires no prior training to use, and consumes less time than the state of the art simulation methods. The main component of energy consumption modeled using the developed software is the Heating, Ventilation and Air Conditioning [HVAC] operation. The HVAC system is modeled using design and analysis methods developed by ASHRAE [Radiant Time Series], coupled with real-life time series solar data from NSRDB. This toolset will help architects evaluate design features like the placement and size of fenestration, materials for construction, building orientation, and more, to enable early design decisions to improve the building’s lifecycle energy consumption.

Temperature in an air conditioned room is not usually even and it is generally known that “the higher, the warmer” in winter and “the closer to the AC, the cooler” in summer. In order to conceive countermeasures to such unevenness, distribution of temperature and humidity in the room should be monitored. Typical line charts and table representation of sensing data would not be efficient for human to understand the situation if the number of sensors increase. Thus, in this research, a system which visualizes the information obtained in real time with AR technology by monitoring the indoor thermal environment by using Wireless Sensor Network (WSN) was developed. This system allows users to observe thermal environment in a 3D space, in order for them to intuitively grasp updated distribution status of temperature and humidity. The feasibility of this system was studied as an environmental improvement method by an experiment.

Critical Infrastructure (CI) plays a vital role in sustaining fundamental functionalities and services in urban systems. As these CI systems are getting more interdependent, disturbances in one CI system often propagate to other systems, imposing significant cascading and devastating risks on cities. This paper introduces a conceptual framework for modeling interdependencies between CI systems and analyzing the impacts of interdependencies on urban resilience. This framework is composed of a directed network model and a resilience analysis approach. In the network model, different types of interdependencies are integrated through a multilayer network structure. For each layer, the use of directed network and the PageRank algorithm provides an improved representation of node criticality. In resilience analysis, urban disturbances are first classified into two categories based on their paths of propagation. Within each category, disturbance responses of the network can be observed by simulating targeted attacks. This conceptual framework provides a quantitative approach of modeling and analyzing CI interdependencies, which can lead to better understanding of the mechanism of CI interdependencies and provide measures to protect CI against possible cascading failures.

This paper presents a framework for analysis of the impacts of uncertainty on the performance of construction projects using a multi-node, multi-link and dynamic network analysis framework. In the proposed framework, the network representations of construction project organizations are created using a meta-network consisting of different types of node entities (e.g., agents, information, resources, and tasks) and different relationships between these entities. Accordingly, the impacts of uncertain events are captured based on perturbations in the node entities and the subsequent changes in the topological structure of project network. The application of the proposed framework is demonstrated using a case study related to a tunneling project. The results of the analysis are twofold. First, the results identify the node entities that are critical for the success of a project. Second, the results capture the impacts of uncertain events on the performance of the project organization based on simulation of different scenarios. The results highlight the capability of the proposed framework in capturing the complex interactions in project organizations and enabling an integrated analysis of organization vulnerability under uncertain scenarios.

Given the uniqueness and diversity of constraints from each construction site, tower crane planning can be a challenging task, especially in terms of collecting and presenting multiple sets of information with varying levels of detail. Several research studies have been carried out to investigate information categories necessary for tower crane planning. This paper investigates challenges related to information representation of tower crane planning in a computational environment. Also, a site-specific information representation schema for tower crane planning is presented. Requirements and information resources for each category of information are specified with the objective of visualizing and automating tower crane planning. This information representation schema was tested on a building construction project and is subject to continuous refinement based on additional case studies of building construction projects that have different characteristics and constraints. Uniqueness from individual projects is expected; however, critical constraints and characteristics are commonly shared by the entire building construction field.

In order to meet the CO2 emission requirements of sustainable construction, both energy performance and ‘carbon’ accounting should be taken into account for the construction information. This study proposes an energy-enhanced BIM framework that integrates the carbon finance theory. With the integrated cost evaluation according to the carbon finance theory, the proposed eeBIM-based platform can help achieve lower carbon emission cost while enhancing the energy efficiency and further estimate the carbon consumption of the building design during its construction. The project team thus will be able to evaluate the overall sustainability and develop an optimized building design solution at the design stage. In particular, this paper discusses the analysis of existing sustainable building design systems through intensive literature review, identifies key challenges and gaps remained unaddressed, and introduces the model proposed to address these challenges and gaps.

Mitigation, preparedness, response, and recovery represent the four-phase methodology for emergency management. However, disaster recovery is considered the least understood aspect in the emergency management science and practice. Achieving a sustainable disaster recovery requires the participation of different stakeholders within the host community through the post-disaster planning and implementation phases. Despite the increasing rate and magnitude of natural hazards in the last decades, it is noted the lack of literature for holistic sustainable disaster recovery models that captures the associated stakeholders’ decisions and actions. To this end, this paper presents an agent-based model to study the disaster recovery strategies of the different associated stakeholders. The model demonstrates two main types of agents; (1) the residents of the impacted region along with their strategies for mitigating financial impacts and maximizing individual welfare, and (2) the government– state and regional—along with their strategies to mitigate the disasters impacts and to increase the host communities’ resilience to hazardous events. The authors used a comprehensive social vulnerability index to better guide the investment efforts at the various levels. Ultimately, the agent-based simulation model helps in better understanding the interrelationship between the different stakeholders, and consequently determining the optimum combination of disaster recovery strategies. The model is applied on three coastal counties in Mississippi to adopt more holistic sustainable disaster recovery processes after hurricane Katrina.

Construction lifts for building construction play an essential role in the vertical transportation of various resources. Practitioners often apply zoning to divide a building into a number of vertical zones to which lifts are assigned. For each zone, they configure service floors and lifts in a variety of ways, attempting to maximize lifting efficiency while minimizing costs for lifting operation. Service floors of zones are often overlapped on one or more floors for convenient transit between zones. This paper presents an analytical approach to evaluating the effectiveness of the results of such overlapping configuration, based on lifting time. For this, a simple, yet practical mathematical model is proposed to estimate lifting time. An application example shows its potential for preparing an economical lifting plan.

Facility management (FM) is responsible from the proper functioning of building systems, such as mechanical, electrical, plumbing, and fire protection. The increased level of complexity in building systems means more work for the owner’s FM personnel. The FM work has unique challenges for finding the right resources of information to detect faulty systems and deficient pieces of equipment. This paper is focused on how Building Information Modeling (BIM) can support FM operations for buildings with more complex systems and strict operation and maintenance requirements, such as healthcare facilities. In order to get a better understanding of the dynamic environment in which the FM operates, a period of work shadowing has been arranged with a healthcare building owner. Case narratives are formed as an outcome of the shadowing experience and process models are created to identify the basic workflows. These workflows include the interactions, decision points, documents and information exchange among the owner’s personnel. Based on these process models, information needs have been discussed in order to extend the functionality of BIM and add value to FM personnel’s operation and maintenance activities.

Reducing fuel consumption on roadway networks can have a huge impact on the nation’s economy and environment. Existing ad-hoc transportation planning efforts that allocate limited funding on need-based criteria are insufficient for providing a significant reduction in fuel consumption. Therefore, there is an urgent need for new research to analyze the impact of planning effort on fuel consumption to support transportation’s decision making. This paper presents the development of a new model for estimating fuel consumption in transportation networks under budget constraints by taking into consideration the effect of pavement deterioration on fuel consumption. The model is composed of three main modules to (1) estimate vehicle fuel consumption of transportation networks; (2) allocate limited funding to competing highway rehabilitation projects; and (3) evaluate the impact of pavement roughness and deterioration on fuel consumption. An application example is analyzed to evaluate the developed model and illustrate capabilities of the model. The application result demonstrates the significant impact of highway rehabilitation planning on fuel consumption on roadway networks. This study should prove useful to planners and decision makers in evaluating the impact of highway rehabilitation efforts on fuel consumption.

Numerical simulations using various finite element (FE) software packages have been widely adopted to investigate the seismic performance of large-scale important structures, such as super-tall buildings and large-span bridges. Among these FE software packages, Open System for Earthquake Engineering Simulation (OpenSees), as an open-source FE software program, has increasingly become one of the most influential packages. However, the computational efficiency of the solvers for linear systems of equations (SOE) in OpenSees, which use the direct method, cannot satisfy the demand for numerical simulation of large-scale structures. Consequently, two new parallel-iterative solvers for the sparse SOE are proposed and implemented in OpenSees, based on two graphics processing unit (GPU)-based libraries, CuSP and CulaSparse. The time history analysis of a 141.8 m frame-core tube building and a super-tall building (the Shanghai Tower with a height of 632 m) are performed using the proposed solvers. The speed-up ratio of the proposed solvers is up to 9 to 15, with high accuracy in results when compared with the efficiency of the existing central processing unit (CPU)-based SparseSYM solver in OpenSees. This research outcome can provide an effective computing technology for the numerical analysis of the seismic behavior of large-scale structures.

The recurrent underground utility incidents (e.g., utility strikes and utility conflicts) highlight two underlying causes: failure to comply with spatial rules prescribed in utility specifications and unawareness of utility locations. It is critical to address these two causes to prevent utility incidents. This paper presents a framework that integrates natural language processing (NLP) and spatial reasoning to infer the vertical positions of underground utilities from textual utility specifications and plans, and to automate the utility compliance checking. The natural language processing (NLP) algorithm extracts the spatial rules specified in textual utility documents, and converts the extracted spatial rules to a computer-interpretable format. The spatial reasoning scheme models the spatial components in a spatial rule as topological, directional, and proximate relations, and executes the extracted spatial rules in a geospatial information system (GIS) to automate the depth estimation and the compliance checking. Several examples are presented to prove the concepts.

The ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. Advances in the artificial intelligence and information sciences provide an opportunity to develop query capturing algorithms to retrieve relevant keywords from freight-related natural language queries. The challenge is correctly identifying and classifying these keywords. On their own, current natural language processing algorithms are insufficient in performing this task for freight-related queries. High performance machine learning algorithms also require an annotated corpus of named entities which currently does not exist in the freight domain. This paper proposes a hybrid named entity recognition approach which draws on the individual strengths of models to correctly identify entities. The hybrid approach resulted in a greater precision for named entity recognition of freight entities—a key requirement for accurate information retrieval from freight data sources.

Despite significant advances in Building Information Modeling (BIM) technology for construction planning, existing technology still lack the capabilities of planning and analyzing temporary structures. While temporary structures such as scaffolding and temporary stair towers significantly impact construction safety, they often do not appear in BIM. Temporary structure objects manually inserted into BIM cannot automatically generate information of their impact on construction safety. Understanding this deficiency, this research attempts to generate required temporary structures and analyze associated safety risk automatically. Specifically, this research focused on planning of temporary stair towers used during roof construction activities. For automation, a set of algorithms were created that analyze geometric conditions in BIM, generate required temporary stair towers, and analyze their impact on safety. The algorithms were imbedded into a commercially-available BIM platform and tested on an ongoing construction project. As a result, optimized locations and shapes of temporary stair towers and associated potential safety hazards were identified and visualized in BIM and schedule. The main contribution of this research would be to automatically perform more practical construction safety planning by incorporating temporary structures.

Technical documents are often generated during Architecture/Engineering/Construction (A/E/C) projects and research. Information Retrieval (IR) is a common technique to manage growing technical document collections. However, due to the complexity of technical documents, applying IR directly to technical documents often leads to unsatisfactory results. Many semantic approaches, such as ontology, are applied to enhance the performance of IR. Developing domain ontologies requires human effort and thus, is a time-consuming task. Further, developing automated approaches for supporting ontology development has become an important requirement. Reference collections developed to evaluate IR performance can be found in many IR research studies. They are representative subsets from entire document collections and are labeled by domain experts. In order to enhance IR performance, the authors propose an automated approach to grow the base domain ontology from a reference collection. The authors also validate the proposed approach on an earthquake engineering reference collection, called the NCREE collection. The results show that the proposed approach is effective in enhancing IR performance. In addition, the workload of the proposed approach is also affordable for domain experts and can become a possible solution for the automation of ontology development.

Thisresearch focuses on developing Building Information Modelling (BIM) guidelines and libraries for Islamic Architecture (IA) andaims to enhance and reduce the time and cost ofprojectsthatuse Islamic Architecturestyles. Ourmain objective is to create BIM-driven objectsand strategiesfor Islamic Architecture elementsthat are organized chronologically according to thehistory ofIslamic Architecture. Islamic architecture contains a massive amount of information that has yet to be digitally classifiedaccording to eras and styles. Categories in use include styles and characters, construction methods, structural elements, and architectural components. This part centers on providing the fundamentals of Islamic Architecture informatics by building the framework for BIM models and guidelines. It provides schema and critical data such as identifying the different models, shapes, and forms of construction, structure, and ornamentationof Islamic Architecture for digitalized parametric building identity.

This research focuses on developing Building Information Modelling (BIM) libraries and guidelines for Islamic Architecture (IA). It aims to enhance design and reduce the time and cost for projects that utilize Islamic-styles of architecture. The main objective of Part 2 is to develop BIM-driven (BIM-IA) libraries and guidelines for Islamic Architecture components that are organized chronologically based on Islamic Architecture history. The resultant BIM-IA is intended to be an organized Islamic architecture database that includes the myriad of Islamic building characteristics and styles. The proposed BIM libraries will include most Islamic Architecture forms, styles, characters, construction methods, structural elements, and the various architectural components that can be used projects. Application of this library is demonstrated in an example that designs a mosque. This example shows how the BIM-IA will assist designers when making decisions about mosque design and function while also demonstrating how valuable time and resources can be conserved when searching for optimum design choices. The research strives to advance building informatics by developing parametric digital components and guidelines of Islamic Construction.

The proposed paper intends to provide a technical review between BIM and GIS and measure the different strengths and weaknesses of each approach. Then, the proposed project aims to present a newly developed data integration approach in facilitating the continuous work processes of the two distinct environments. This research utilizes BIM based (IFC) system to store road components data in highway construction and then GIS system to import data such as land boundaries, and topographic data. In order to retrieve and integrate the two distinct types of data formats, this research uses the concept of semantic web in RDF format to provide semantic interoperability between BIM and GIS operations.

Buildings and their systems are primarily designed based on several assumptions about end-users’ requirements and needs, which in many cases are incomplete and result in inefficiencies during operation phases of buildings. With advancements in fields of augmented and virtual reality, designers and engineers have now the opportunity to collect information about end-users’ requirements, preferences, and behaviors for more informed decision-making during the design phase. These approaches allow for buildings to be designed around the users, with the goal that the design will result in reduction of energy consumption and improved building operations. The authors examine the effect of design features on occupants’ preferences and performance within immersive virtual environments (IVEs). Specifically, this paper presents an approach to understand end-users’ lighting preferences and collect end-user performance data through the use of IVEs.

Since the Occupational Safety and Health Act of 1970 was established, which places the responsibility of construction safety on the employer, various injury prevention strategies have been developed and resulted in a significant improvement of safety management in the construction industry. However, during the last decade, construction safety improvement has decelerated and, due to the dynamic nature of construction jobsites, most safety management activities have focused on safety monitoring during the construction phase. Traditional safety planning approaches rely mostly on static information, tacit knowledge, regulations, company safety policies, and 2D drawings. As a result, site-specific dynamic information, temporal (e.g. when and who will be exposed to potential hazards) and spatial (e.g. location of dangerous zones) information are currently not specifically addressed. This paper presents a formalized 4-dimensional (4D) construction safety planning process that addresses site-specific temporal and spatial safety information. The safety data, which includes general safety knowledge, site-specific temporal and spatial information, will be integrated from a project schedule and a 3D model, respectively. The proposed safety planning approach is expected to provide safety personnel with a site-specific proactive safety planning tool that can be used to better manage jobsite safety. In addition, visual safety materials can also aid in training workers on safety and, consequently, being able to identify site-specific hazards and respond to them more effectively.

An essential part of construction project planning and control is the development of a model of the project’s construction processes. Discrete-event simulation is the most versatile of all modeling methods, but it lacks the simplicity in use of other modeling techniques and so has not been widely adopted in construction. The Critical Path Method (CPM) is the most popular project modeling method in construction since it is simple to use. However, it is limited to time-dependent constraints and does not handle work repetition very efficiently. Many other modeling techniques have been developed over the years but have a limited scope of application, being aimed at specialized types of project. Linear scheduling is one such example, being limited to work that progresses along a line, but is otherwise very easy to use and provides powerful visual insight into the interactions between crews and the performance of a project. Foresight is a new graphical constraint-based method of modeling construction processes, designed to offer the simplicity in use of CPM, the visual insight of linear scheduling, and the versatility of simulation. This paper compares Foresight with discrete-event construction simulation in terms of the simplicity of the resultant models for manufactured and on-site based produced components. A qualitative comparison of the two approaches is also included highlighting the superior visual insight provided by Foresight, a feature that facilitates verifying a model and determining an optimal project plan.

Tower cranes are widely used in construction jobsite for their efficiency. However, tower cranes and construction workers themselves suffer a significant safety hazards from natural sway of payloads. Besides, the external disturbance of wind leads to additional sway and intensifies the oscillation amplitude of crane load on construction site. Therefore, we propose a hybrid control mechanism that combine electronic and mechanical gyroscopes to produce a balancing torque, keeping crane load stable. We assume the crane load as in the case of the inverted pendulum and simulate the oscillation movement under continuous wind. A hybrid control mechanism is designed and developed, with the electronic gyroscope to track the real-time position and orientation of payload, with the mechanical gyroscope as an actuator of collected feedback to control the oscillation amplitudes. A wind tunnel test has been conducted to validate the developed hybrid mechanism.

A seismic damage simulation of regional buildings can provide valuable information for earthquake disaster prevention and mitigation. A critical issue for such a simulation is to select an appropriate numerical model that balances accuracy and efficiency for an individual building. Therefore, a moderate-fidelity model, named the multi-story concentrated-mass shear (MCS) model, and an associated parameter determination approach based on the HAZUS performance database are proposed for the simulation, with which the seismic responses of each story can be predicted through a nonlinear time-history analysis (THA). The reliability of this model is validated by comparison with the results of refined finite element (FE) models. Furthermore, the seismic damage simulation of the Tsinghua campus, which includes 619 buildings, is performed. A 40 s nonlinear time-history analysis of the 619 buildings can be completed in 15 s on a desktop computer by using the proposed method. Finally, the seismic responses of the whole area are vividly displayed through a visualization interface. The different damage states for each story are clearly identified for every individual building. The research outcome will assist in providing a useful reference and an effective tool for quick damage assessment and emergency management.

Discrete event simulation (DES) is widely regarded as an effective tool for modeling, analyzing and establishing the correct design of construction operations, including the proper quantity and sequencing of resources within the context of a selected field construction method. However, a gap exists in current construction operation studies with respect to human factors, specifically, workers’ physical aspects. Construction workers, one of the most important resources in construction operation, have limited physical capabilities due to fatigue depending on individual- and task-related factors. However, less attention to fatigue has been paid in DES studies in construction, despite its significant impacts on construction performance. To understand dynamic impacts of workers’ physical constraints on construction performance, we propose worker-oriented construction operation simulation integrating a fatigue model into discrete event simulation. Specifically, workers’ activities during construction operations are modeled using DES in an elemental task level. Then, physical demands from the operation are estimated through biomechanical analysis on elemental tasks. Fatigue, which refers to diminished physical capabilities under given physical demands, is predicted using a fatigue model, and its impact is subsequently reflected in DES. As a preliminary study, we tested the feasibility of the proposed approach on masonry work by varying crew size. The results indicate that excessive physical demands beyond workers’ capabilities result in productivity losses. Ultimately, the proposed approach has the potential to decide alternatives for construction operation planning to secure high productivity without compromising workers’ health.

The objective of this study is to develop a proof-of-concept framework to quantify the impact of the anticipated rise in temperature and humidity change on construction productivity at a project level as a result of global warming. The developed framework applies building information modeling (BIM) integrated with labor productivity information, critical path method (CPM) schedule, and a temperature-and-humidity-versus-labor-productivity model to simulate the global warming effect on a structural steel erection model project. To demonstrate the application of the developed framework, one domestic and six international cities’ 2090-2099 climate projection data, derived from existing global climate models with different greenhouse gas (GHG) emissions scenarios, were used as input data for the simulation to quantify the workhours required to build the model project. The results for the selected locations show that the impact of global warming on construction productivity varies with geographical location and that different climate models produce varied results.

This research aims to define an approach to identify construction-related design information at early design stages. Those information can be extracted from a building information model and used to support an automated, rule-based constructability checking in a proactive manner. Focusing on formwork construction of cast-in-place concrete building structures, this paper uses a case study and applies the object-oriented paradigm inherent in a BIM model as the opportunity and mechanism for leveraging constructability reasoning that is captured from the structural design process and construction expert’s knowledge. The hierarchical model of product information for the case study building is presented and then aligned with the formwork decision-making for product realization. The accessibility of the construction-related design information, in terms of level of definition as well as representation within in the design model, is discussed, indicating the needs for system information progression since early design stages to support the BIM-enabled, automated review.

Currently, most infrastructure inspection standards require inspectors to visually assess structural integrity and log findings for comparison during future inspections. This process is qualitative and often inconsistent. Furthermore, changes in inspection protocols over time can create discontinuities in understanding the time-history of a structure. This paper presents a systematic technique for capturing and representing inspection data, leveraging a newly developed hierarchical computer vision methodology. This technique can be used to improve the level of accuracy in condition assessment procedures by allowing inspectors to recreate the structural inspection scenario on a computer through a high-fidelity virtual environment. The methodology presented herein utilizes adaptations of the Dense Structure from Motion (DSfM) algorithm, which reconstructs three-dimensional (3D) scenes from two-dimensional (2D) digital images. In order to produce highly-accurate and photorealistic 3D reconstructions there are four core stages: (i) an image capture plan that covers all views of the structure and emphasizes critical details, (ii) reconstruction of an initial dense point cloud to generate a geometrically accurate 3D model, (iii) reconstruction of separate, higher density point clouds of critical details or suspected deficiencies, (iv) application of robust computer vision algorithms to hierarchically register and merge the point clouds. The result of this approach is a virtual 3D model of the structure with accurate geometry and high-fidelity representation of fine details. The accuracy and adaptability of the developed technique was compared to both conventional DSfM reconstruction methods and terrestrial 3D laser scanning (TLS). The experimental validation indicates that the hierarchical technique produces denser and more comprehensive models with an accuracy of one tenth of a millimeter, an order of magnitude improvement over either conventional DSfM or TLS.

The AEC/FM industry has benefited from the innovative integration of information technologies and industry-wide processes in different lifecycle stages of facilities. Building Information Modeling (BIM), as one of these innovations, is fast becoming a key approach to virtually integrate the required information for facility design, construction, and management. So far, applications and benefits of using BIM tools and processes in building design and construction have been documented in research. However, landscape design and construction practice is underrated in current BIM developments and in integrated design-construction practices, and it has not benefited from the advantages BIM provides to the industry at different scales. This could result in a critical challenge, as BIM implementation and information modeling are becoming mandatory in many projects in public and private sectors, and the gap still exists in the processes of collaboration and information exchange between the landscape design and construction practice and other disciplines. As an early step to mitigate this challenge, this study shows that recent advances in BIM, COBie, information-exchange schemas (e.g. IFC), and taxonomies such as OmniClass have shortcomings in addressing landscape and hardscape elements and attributes. This challenge limits asset-management capabilities, and leads the practice to inefficient operations, more manual processes, and costly knowledge development and exchange. These findings have important implications for revising and updating existing taxonomies to support more automated information development and exchange processes.

Challenges encountered by facility managers include developing visual models in their mind to corroborate the relationship between two dimensional (2D) as-built drawings with the real world three dimensional (3D) elements, searching and validating information. These challenges can be addressed by using Building Information Model (BIM) in the O&M phase. Accessing the information using BIM is a two-step process. The first step includes the identification and selection of the appropriate 3D element from the digital model and second step includes the retrieval of the information. Currently, the first step is accomplished by navigating the model manually. This step becomes tedious depending upon the size and complexity of the model. This identification selection process can be automated by integrating Quick Response (QR) code technology with BIM. This paper discusses the feasibility of developing an integrated BIM and QR code environment. It also discusses the pilot study conducted to deploy this environment.

The importance of end-user participation in the design process of building and construction projects has been recognized and addressed by a number of researchers and practitioners. The main goal is to ensure that the project outcome meets the facility users’ needs. In order to understand their needs, a variety of approaches (e.g. focus groups, workshops, and questionnaires) for the building end-users participation in the design process have been presented in the literature. Despite the contributions and practical features of these methods, they require a significant amount of time and effort to conduct and interpret the participants’ responses. To overcome this limitation, this paper investigates the use of eye-tracking technology to measure and analyze end-user satisfaction. This study is carried out to test the hypothesis that the users’ satisfaction of design variations is related to their visual attention. In other words, design alternatives with high level of users’ satisfaction attract more attention. An experiment using four alternatives for the design of a façade is performed to test the effectiveness of eye-tracking technology. The design alternatives are developed and displayed in a virtual 3D environment. Participants are asked to rate their level of satisfaction with each alternative, while their interaction with the virtual models is recorded using eye-tracking. The results of the experiment are also demonstrated to domain experts to get a better understanding of the technology’s potential and challenges.

Client-developed building programs are increasingly burdened with a number of requirements such as building codes, safety, and programming requirements. Managing these requirements ensures the successful performance of subsequent procedures and the high quality of a project. However, because assessing a building design relies primarily on the competence and dexterity of an architect, domain experts often do not discover that these requirements have not been met until late in the construction phase. For example, a hospital design, which has a complex structure or complicated requirements, should be evaluated with multi-functional features that help maintain the consistent quality of the design. Therefore, to support the evaluation of a building design, this paper proposes automated rule-based checking that assures satisfaction of the requirements of a spatial program and visibility. This rule-checking process uses the extended rule language of the building environment rule and analysis implemented on the Solibri Model Checker.

According to the U.S. Energy Information Administration (EIA), building and transportation sectors account for approximately 75% of CO₂ emissions. Given the magnitude of this statistic, many studies have been directed towards the issue of energy use and carbon emissions of the built environment. Most of these studies however, have focused only on either buildings or transportation systems. To analyze the dynamics of energy use associated with buildings and transportation systems, it is essential to explore the interactions between these two sectors in a single comprehensive model. This paper develops a network infrastructure model to determine the transportation energy intensity of a building as well as building energy consumption based on the residents’ lifestyle. The proposed model is developed using Geographic Information Systems (GIS) and Building Information Modeling (BIM) to identify the current trends in energy use associated with people behavior and infrastructure (buildings and transportation networks). BIM is used as a life cycle inventory to model and collect building-related information and material quantities, and GIS is used to define geo-referenced locations, storing attribute data, and displaying data on maps. The main input to the model would be characteristics of buildings and transportation networks, and socioeconomic data (population dynamics) collected from a survey. The model then generates the energy and carbon implications of the network in the form of a map.

The rapid deterioration of infrastructure systems along with the shrinkage of funding resources necessitates cost-effective management of infrastructure networks. The existing methods for cost analysis of infrastructure networks are based on optimizing network costs for a limited period, and hence, are prone to: (i) shifting the cost burdens, (ii) not considering the service life of assets in a network beyond the planning horizon and (iii) not considering uncertainty in factors such as future preservation funding as well as timing and cost of preservation activities. In this paper, we propose a simulation framework to address these limitations in network-level cost analysis. The proposed framework is based on the premise that a sustainable practice is the one that provides the longest service life at the lowest cost in infrastructure networks. The proposed framework determines the network-level costs considering the dynamic network-agency-user interactions and uncertainties. The application of the proposed framework is demonstrated using a case study pertaining to a pavement network. The results show the capability of the proposed framework in evaluating and identifying sustainable strategies leading to the longest service life for the assets at the minimum network-level costs.

In an effort to document work-in-progress, many construction companies take hundreds of images on their project sites on a daily basis. These images together with 4D BIM can serve as a great resource for analyzing progress deviations. To facilitate image-vs.-BIM comparison, several methods have been introduced that tie in all images together in 3D using standard Structure-from-Motion (SfM) procedures. The resulting point clouds are then superimposed with the 4D BIM, resulting in back-projection of BIM on all images that were successfully registered through the SfM procedure. However, often site images exhibit wide baselines and thus are not successfully registered with BIM. To address current limitations, this paper presents a method together with experimental results that leverages BIM as a priori to initiate the SfM procedure. It is shown that by interactively guiding BIM into one or a few images that have significant overlap with the rest, the proposed BIM-assisted SfM procedure results in more complete point clouds and also generate more accurate BIM overlays on site images.

Both the use of Building Information Modeling (BIM) technology and lean philosophy provides gains in the application of the lean principles. This paper presents the results of the application of an integration framework for the use of BIM with Last Planner System^TM. These results were obtained from a real world using of BIM technology in two residential projects in the city of Curitiba (Brazil). Firstly, it was held a planning and control processes diagnostic; after this, the LPS implementation and the development of the BIM models were done; and finally, the use of the integration framework of BIM with Last Planner System^TM. The use of BIM model in production management assisted in the decision-making on short and medium term planning and control. It was implemented and electronic communication (online or offline) for visualizations of work package process status and 4D visualization of construction schedules. The implementation of BIM and LPS in these two projects demonstrates some specific interactions between BIM and LPS proposed in literature.

Performance-based fire safety design in buildings has been widely accepted throughout the world. However, performance features for safety design in building have traditionally been designed using historical statistical data, or just the data from previous experiences and do not always accurately correspond to the unique characteristics of the building. Evacuation performance is one of the main factors to be determined and is the most challenging parameter due to the complex human behavior exhibited during the evacuation process. As the complexity and innovation in modern building design increases, the historical methods to determine the evacuation performance features in safety design are becoming insufficient. Moreover, in reality, it is impossible to conduct an evacuation experiment to determine the evacuation performance before construction, and is also difficult to collect the accurate features in a simulated building condition during the prescribed emergency situations. A new method, a building information modeling (BIM)-based Immersive Serious Gaming environment is proposed, which provides an immersive emergency evacuation scenario to solicit the behavior decisions made by the players. Evacuation performance data is contemporaneously from the players playing the game. The interaction between occupants (players) and the building during evacuation are studied to determine evacuation performance and provide behavioral data for the evacuation performance evaluation of the building design. This research will extend the study and adaptability of serious computer games in performance-based safety design.

Building information modeling (BIM) has recently experienced rapid technological advancement and gained prevalence in the architectural, engineering, and construction (AEC) industry. BIM models contain valuable information on buildings to support four-dimensional (4D) modeling that involves the integration of three-dimensional (3D) building components and time as specified in the one-dimensional (1D) construction schedule. While 4D modeling has been proven to be useful in effective construction management, a critical challenge in current 4D modeling practice is the discrepancy between the element breakdown structure (EBS) of the BIM model and the work breakdown structure (WBS) of scheduling. This discrepancy leads to considerable time and effort being spent on matching and linking BIM model components and schedule activities. This paper presents a framework for automating the generation of construction schedules during the BIM model creation process. A matching and pairing mechanism between EBS and WBS was created to automate the matching and linking between BIM model elements and construction schedule activities. A prototype system was developed and tested to validate this framework. The proposed framework is expected to automatically generate detailed construction schedules during the development process of BIM models.

This paper proposes a framework for autonomous data-driven discrete-event simulation (DES) modeling process that transforms operation data to Activity Cycle Diagram (ACD)-based DES models without a priori model network. The proposed framework uses the idea of process mining to discover a real process by extracting knowledge from activity logs, which are the lists of sequences of activities per cycle in terms of resources. The discovered process is then modeled as an ACD which can replicate the underlying activity logs. Activity log plays a key role in this procedure as it determines the set of activities that we can identify and thus govern the analysis scope of the generated simulation model. It is therefore important to design and collect activity log data in a way to capture all the activities for an intended abstraction level. In order to automate the model generation procedure considering this requirement, we employ the notion of activity ontology, which specifies various activities and their relations for a given construction operation at different abstraction levels in a predefined manner. Using the ontology-based framework, we can systemize the procedure of simulation modeling, which entails selecting the abstraction level of simulation model, identifying the list of activities that need to be inferred from data to create activity logs, and determining the type and resolution of data to be collected. The proposed framework is explained and demonstrated using an earthmoving operation example.

Generating the structure of a simulation network is one of the most important steps in the process of modeling construction operations using discrete-event simulation (DES). It is, however, a complicated and time-consuming task that requires extensive expert knowledge and data pre-processing, which are needed to establish plausible assumptions to build the network. Often such assumptions fail to capture reality, producing a large discrepancy between the generated simulation model and the underlying actual operation, due to an absence of data or incomplete knowledge of the modeler or subject matter expert (SME). As an alternative solution, this paper proposes an approach to learning the simulation model structure from data. We introduce techniques for discovering workflow models from activity log data, which are time-ordered records of all the activities performed by various types of machines during a given construction operation. The latest advancements in data collection and processing techniques such as activity recognition algorithms have made it possible to harvest activity logs based on sensor-based time series data collected from construction equipment. Since activities are fully ordered and recorded sequentially, these activity logs can be used to construct a process specification which adequately models activity cycle diagram (ACD). We introduce a refined α-algorithm to extract a process model from such log data and represent it in terms of an ACDbased DES model. This paper demonstrates the proposed method in the context of earthmoving operations and shows that it can successfully mine the workflow process of the earthmoving operations represented by an ACD.

Thorough condition assessment of America’s aging bridges is necessary as they are either nearing their service lives or have already passed it. It is a challenge to abstract relevant information from heterogeneous bridge data sources, such as previous inspection reports, as-built drawings, to develop a thorough understanding of the current and historical condition of a bridge. This can limit decision-making capabilities and efficiency of an inspector during condition assessment. Current standards, such as Industry Foundation Classes (IFC) and more recently, IFC-Bridge, allow representation of spatial and physical features of a bridge. However, they do not contain explicit representation concepts that can integrate inspection-specific details (e.g. element condition ratings, repair history etc.) with spatial and physical information from a bridge information model, which would help in condition assessment. In this paper, we identify requirements of such an integrated model, and discuss possible approaches to develop such a model by leveraging and extending existing IFC-Bridge standards.

Existing resource scheduling methodologies are insufficient for controlling workflows for individual craft persons in project and workface planning. In practice, the workflow of an individual resource is assigned by a project manager in consideration of the resource supply and resource demand for particular time periods of the project duration. This research study proposes a crew-job allocation methodology to facilitate scheduling and resource management at both project and workface levels. We propose the use of a mathematical model to formulate and solve for the identified problem factoring in resource-time tradeoff options on individual activities. Then a crew-job interaction table is instrumental in visualizing the optimum resource-loaded schedule, which features the shortest project duration and the leanest resource supply under time-dependent resource constraints. Case studies are given to illustrate application of the proposed methodology. This technique potentially provides analytical decision support for not only making cost-effective resource-loaded schedules, but also facilitating the controllability of schedule execution.

As image-based assessment practices become increasingly present in the field of civil engineering, so do the interpretation errors. In machine vision, these errors are typically represented by quantitative metrics stating the presence of error. In order to provide a comprehensive assessment of the state of a structure and structural elements, errors should be interpreted and rationally integrated into decision making. In this paper, a performance-based approach to include the source and impact of the errors in machine vision object detection techniques is presented. Structural element evaluation is used as an example. The approach involves an adaptation of the error-domain model falsification method developed previously. More specifically, an automated method in reinforced concrete column damage state estimation based only on visually observed damage characteristics is used as a study case. Predicted damage state(s) (eleven in total, DS0-DS10) are compared with the measured damage state of the column surface image that has been retrieved by way of the automated machine-vision method. Ultimately, a set of damage states is provided as equally possible solutions for the input image of the column surface. This analysis helps focus efforts to reduce errors on image characteristics that are the greatest source of ambiguity. In this study, lower errors associated with determining damage states 0 to 5 would be the most helpful.

Currently, utility information such as alignment and owner information is mostly managed in 2D CAD files or 2D geographical information system (GIS) models, which have limited capacity for analyses and are sometimes not updated. This paper presents an integrated 3D framework based on building information modeling (BIM) and GIS technologies for managing and analyzing utility information. In the framework, underground utility networks are modeled and represented in GIS software to facilitate visualization. Semantic information such as owner information and inspection records are also included in the 3D representations of utilities in GIS software. The GIS models are then integrated in a BIM environment based on ArcGIS using our developed BIM-GIS integration framework. The BIM-GIS framework supports various analyses such as clash detection. Clashes between existing utility networks and the designed pipelines can be identified and resolved virtually ahead of time. Changes of the utility models can also be updated conveniently in the integrated BIM-GIS framework. An example scenario that is used to test and evaluate the developed framework will be presented in this paper.

There is an increasing recognition that, to maximize the benefits of Building Information Modeling (BIM), it should be deployed in such a way that it remains useful beyond the design and construction phase of projects. This means that it should be utilized in the facilities management phase of a constructed facility. This paper focuses on the identification of benefits gained from the effective integration of BIM in Facilities Management. Drawing on literature from previous research, it argues for the adoption of strategic approaches towards holistic consideration of benefits. The paper also presents the findings of a preliminary case study based on exploratory interviews with key personnel on a major university building project, and review of project documents. Qualitative descriptions of benefits were obtained from these interviews and are summarized in the paper. The paper concludes with a discussion of the benefits of BIM/FM integration, with a view to a comprehensive determination of project gains within the lifecycle of a project. The paper offers some conclusions that are aimed at the holistic consideration of benefits through the project phases preceding handover and including those in the lifecycle stage for a more wholesome assessment of the effectiveness of BIM implementation in FM.

The potential for automated construction quality inspection, construction progress tracking and post-earthquake damage assessment drives research in interpretation of remote sensing data and compilation of semantic models of buildings in different states. However, research efforts are often hampered by a lack of full-scale datasets. This is particularly the case for earthquake damage assessment research, where acquisition of scans is restricted by scarcity of access to post-earthquake sites. To solve this problem, we have developed a procedure for compiling digital specimens in both pre- and post-event states and for generating synthetic data equivalent to which would result from laser scanning in the field. The procedure is validated by comparing the physical and synthetic scans of a damaged beam. Interpretation of the beam damage from the synthetic data demonstrates the feasibility of using this procedure to replace physical specimens with digital models for experimentation and for other civil engineering applications.

Building Information Modeling (BIM) is gaining wide-spread acceptance in the Architectural, Engineering and Construction (AEC) industry. The implementation of BIM has focused largely on Architectural, Structural, as well as specialist Mechanical and Electrical disciplines, but it remains perceived mainly as a design tool or at best a visualization tool in 3D or 4D. Instead, we have to go beyond BIM and begin to embrace the notion of virtual design and construction (VDC). It is a concept or approach to build, visualize, analyze, and evaluate project performance virtually and early before a large expenditure of time and resources is made. Although BIM could form the backbone of this approach, its success requires a relook of the processes. Intelligent Project Realization (IPR) is introduced which characterizes the ‘science’ behind this approach, with an analysis methodology supported by Multiple Domain Matrices (MDM). There is also a lean perspective that should govern this transformation in project delivery.

Crane operation is one of the most essential activities on construction sites. However, operating a crane is a sophisticated job which requires the operator to have extensive skills and experience, and most importantly a comprehensive understanding of crane motions. Besides typical crane motions such as boom slew, hoist, and extension, monitoring and controlling the position of the load is extremely important to avoid struck-by accidents caused by crane load, especially when the load swings as a result of wind and inertia. Although typical motions can be captured by some existing techniques, a reliable approach to position the load and monitor the load sway remains missing. This study proposes an orientation-based approach for tracking crane load position and monitoring load sway in daily lifting activities. This approach adopts an off-the-shelf inertial measurement unit (IMU) module for measuring load orientation, and an efficient algorithm for converting orientation measurements to load positions. The proposed approach was tested in two load sway scenarios in a controlled lab environment. Test results indicate that the proposed approach correctly converted orientation measurements to accurate load positions and reconstructed the load sway trajectory in both linear and circular sway motions. Enabling continuously monitoring of crane load motion, this approach augments the crane motion information obtained by typical crane motion capturing systems. The findings in the research will advance the safety practices in crane lifting activities.

There is little doubt that the implementation of sustainability in the construction industry is currently an obligation. Despite all the initiatives that are being promoted internationally, there is still much to be done. This development will require new procedures and a change of thinking in the industry. A global approach which considers construction during its whole life cycle from a triple perspective is essential. Building Information Modeling (BIM) may be seen as a suitable methodology for the industry’s transformation towards sustainability. In order to achieve a wider perspective during the decision-making process, environmental information can be combined with BIM. The integration of environmental criteria in smart objects contained in different BIM libraries is surely a promising initial step. This paper will present an evaluation of such an approach and highlight its respective strengths and weaknesses.