Journal of Construction Engineering and Management

Rapidly growing construction markets in China and the United States have provided opportunities for contractors to venture into foreign industries. To help project managers understand the fundamental difference in culture and the way projects are managed, it is useful and critical to reveal and compare the causes of delay and construction management practices in China and the US. The research is aimed at identifying and comparing the most prevalent reasons for task-level delay between the two countries, as perceived by various levels of management, and understanding how the culture and tradition influence management practice and performance. A questionnaire survey for public projects contracted to civilian construction companies was administered to contractors, 150 usable responses from China were collected, and the results were compared to the results from a similar survey in the US. The contributions are quantitatively examining the essential constituents causing task delay, discovering the root causes and key differences in construction management culture between the two countries, and developing strategies to help contractors minimize possible task delay when working in different culture and environments. The findings will help construction managers learn the essential differences in culture and their impact on the ways of project operation so that they can take proactive actions to interact with project managers from different counties to minimize delays.

An appropriate material-management strategy requires estimation of, and classification based on, the lead time of construction materials. To estimate the lead time precisely, the identification of the factors that influence it is essential. Previous studies in material management have not comprehensively examined the factors that influence lead time and have not emphasized the need to classify materials accordingly. To address these gaps, this study collected a large sample of procurement data from 16 building construction projects. A two-step cluster analysis resulted in three clusters, namely, long lead (L), moderate lead (M), and short lead (S), referred to here as LMS based on the average lead time of the materials. Combining the LMS classification with the existing ABC classifications can facilitate more reliable control over the inventory of materials. Furthermore, the regression analysis of the procurement data revealed that the lead time of construction materials is positively correlated with the unit price and order value but negatively correlated with the project value. Therefore, the lead time increases with the increase in unit price and order value of materials while it decreases as project value increases. Furthermore, the capacity of a supplier was found to have a negative influence on the lead time of bulk materials. Therefore, bulk materials delivered by a supplier with higher capacity will have a shorter lead time than those delivered by a supplier with lower capacity. The findings of this study enable construction practitioners to precisely estimate the lead time of materials and to adopt the appropriate strategies for managing construction materials, thereby enhancing the availability of materials for the project.

Measuring three-dimensional (3D) building expansion and quantifying both horizontal and vertical dimensions in a nonautomated manner may not be possible for big data sets. This paper aims to present novel metrics for measuring both vertical and horizontal developments and to develop a web-based app and a dashboard workflow which identifies differences in the form and scale of buildings. The novel 3D metrics are developed based on the concepts of contact surface and 3D discrete compactness. A workflow was developed exploiting the potential of advanced geographic information system (GIS) functions, along with airborne lidar data processing, to apply the metrics on both a simulation of big data sets and digital building models from point clouds. The database system, including a geo-web-based app, is developed for visualizing 3D compactness computations on a dashboard and sharing the outcome with practitioners using their own smartphones. In the simulation experiment, nine 3D building block types were created, and the computation results were compared with the same size building blocks, which were derived from lidar point cloud data sets. The computation results of both the simulation and airborne lidar data, which included 123 and 127,407 data points, respectively, show that the novel metrics are able to identify differences among the typologies and intermediate 3D patterns. The presented workflow also enables online visualization of the computation output.

The data collected by unmanned aerial vehicles (UAV) provide unique opportunities for applications, such as construction progress tracking and facility monitoring. To increase the effectiveness of UAV-captured data, inspection mission plans should be designed prior to site visits. The data collection locations must be identified and adjusted based on the user’s objectives. The UAV flight mission must guarantee a visit to each identified location, and the inspection plans must satisfy the constraints imposed by safety requirements, no-fly zones, and UAV’s limited battery life. This problem becomes more complicated indoors due to complex and continuously changing building layouts. This work uses four-dimensional (4D) building information models (BIM) to automatically design optimal UAV mission plans in support of indoor UAV-enabled data acquisition. It automatically identifies the inspection targets based on users’ customizable and multicriteria description of objectives (e.g., columns behind schedule). The three-dimensional (3D) navigable space is automatically calculated by time stamping the 4D models based on the inspection date. The navigable space is further refined based on safety rules. An optimal UAV inspection mission plan is developed using swarm intelligence that ensures complete coverage of targets and minimizes battery use. The method is based on the industry foundation classes (IFC) schema, promoting OpenBIM and interoperability, which are core challenges in the construction information modeling domain.

The highway network is an economically necessary form of transportation that has a significant impact on the quality of the life of the citizens who use it. Cost overruns in highway projects have been a universal occurrence that jeopardize the development, maintenance, and expansion of this vital infrastructure. Incorrect cost estimations can drive decision makers to pass ineffective policies that have played a large role in the cost overruns of transportation construction projects. The existing prediction models in the literature are limited in one or multiple areas of modeling approach, inputs, and model development robustness. In this research, a model was developed to accurately predict the total construction cost of highway projects by utilizing machine learning algorithms. This study developed a modeling pipeline to automate much of the cost forecasting process, reducing the amount of manual work and dependence on skilled data scientists. This study used the Florida Department of Transportation’s (FDOT’s) critical highway construction cost items between 2001 and 2017 to test the model. The highways of Florida were selected for testing due to the states’ population growth, high immigrant population, logistics, and hurricane frequency. This study used a pool of five categories of independent variables (69 variables total), including the construction market, energy market, socioeconomics, US economy, and temporal variables, which were compiled from relevant sources and existing literature. The results revealed that our linear model exhibits superiority in generalization and prediction of cost items over nonlinear models and is capable of accurately forecasting highway construction costs. Our suggested approach in this study also provides more accurate forecasts for the detailed cost estimation by considering the monthly historical information for the average 92.6% of the six highway construction types mentioned with a 92.51% prediction accuracy. By employing our developed model, local governments, network operators, contractors, and logistics sectors would be capable of a more exact prediction of highway construction costs.

Fugitive dust at construction sites reduces air quality and can affect respiratory health. Contractors must often use dust control strategies to meet environmental regulations for dust mitigation. Water application is the most common mitigation strategy, although alternatives such as magnesium chloride (${\mathrm{MgCl}}_2$) are also used. Although these strategies reduce fugitive dust emissions at the site, they cause other environmental impacts onsite and off-site throughout their life cycle. A new approach to dust mitigation, enzyme-induced carbonate precipitation (EICP), has been developed to improve dust mitigation while reducing life cycle impacts. A life cycle sustainability assessment (LCSA) was performed to compare common dust mitigation strategies with EICP across a variety of environmental impacts and cost. LCSA has not previously been applied to dust mitigation strategies, so this work contributes new knowledge on the sustainability of existing and novel approaches to dust mitigation. The results show that EICP is potentially more sustainable than water application, particularly as watering frequency or persistence of the EICP treatment increases. Compared with ${\mathrm{MgCl}}_2$, EICP may be more environmentally intensive and more expensive. However, indicators for salinization of water and soil, impacts of concern for ${\mathrm{MgCl}}_2$, do not exist, making the comparison incomplete. It is anticipated that with further research and development focused on preventing EICP process emissions, enhancing the durability of EICP treatment, and reducing production costs, the technology will become more favorable for fugitive dust control.

This paper considers the problem of estimating road user costs (RUCs), such as travel time and delays, for roadway construction projects under time and data limitations. RUCs measure the mobility impacts of construction work zones on public roads. RUC metrics are typically used in construction phases to determine the project’s incentives and disincentives. However, determining RUC can be challenging due to scarce traffic data availability, incomplete construction plans, and limited time to conduct analysis. In this paper, we use traffic microsimulation to estimate the RUC for each hour of a typical 24-h day using 11 construction cases in Dallas, Texas. Our primary contribution includes developing relationships between peak-hour RUC measures with respect to the total daily values. Such relationships would allow an estimation of the full day RUC based only on the analysis of the peak hour. The main results suggest that the peak-hour travel time as a fraction of the daily value is, on average, 8.2%, comparable to typical percentages of the peak-hour traffic volumes. Furthermore, the models for predictive relationships using construction scenario demand and capacity show high correlation coefficients, presenting a potential for the development of such relationships for roadway construction projects.

Workers in industrialized construction are often exposed to physical demands (e.g., repetitive motion and awkward body posture) associated with the risks of work-related musculoskeletal disorders, despite the utilization of automated equipment. To reduce exposure to these risks, an investigation of workstation design is needed. This paper proposes a virtual reality (VR)-based ergonomic assessment method that identifies ergonomic risk ratings proactively in the initial phases of workstation design, thus reducing the need for physical mock-ups and lessening the cost and time required to develop and implement an improved workstation design. This method was demonstrated in an operational task through an experiment conducted with 13 participants. Compared with the physical mock-up, the designed VR application achieved an accuracy of approximately 86% and 80%, respectively, in Rapid Upper Limb Assessment and Rapid Entire Body Assessment to assess risk. Moreover, a questionnaire was developed to obtain feedback from participants on their perceptions toward the designed VR application, which further supported the success of using the proposed VR-based ergonomic assessment method.

Monitoring and understanding construction workers’ behavior and working conditions are essential to achieve success in construction projects. The dynamic nature of construction sites has heightened the awareness of the need for improved monitoring of individual workers on sites. Although several studies indicated promising results in automated motion and activity recognition using wearable motion sensors, their technical and practical feasibility was not properly validated at actual job sites. Motion recognition models have to be evaluated in actual conditions because the motion sensor data collected in controlled conditions, and actual conditions can have different characteristics. This study proposes Long Short-Term Memory (LSTM) networks for recognizing construction workers’ motions. The LSTM networks were validated through case studies in one bridge construction site and two road pavement sites. The LSTM networks indicated classification accuracies of 97.6%, 95.93%, and 97.36% from three different field test sites, respectively. Through the case studies, the technical and practical feasibility of the LSTM networks was properly investigated. With LSTM networks, individual workers’ behavior and working conditions are expected to be automatically monitored and managed without excessive manual observation.

Deferred maintenance (DeM) backlogs can accelerate facility deterioration, which can cause buildings to reach demolition conditions prematurely. Although higher-education institutions allocate DeM costs to the budget, they lack a strategic decision-making process to address the DeM backlog issue. This paper provides a new paradigm to evaluate and diagnose the DeM requirements at the system, subsystem, and component levels. The proposed total-package-prioritization (TPP) mitigation strategy for the DeM consists of five phases: (1) building selection, (2) system evaluation, (3) DeM component evaluation, (4) DeM subsystem model evaluation, and (5) total subsystem evaluation. The key performance factor was reliability, to evaluate the facility condition at the different levels. Probabilistic risk assessments, including fault tree analysis and Bayesian network analysis, were applied to develop the TPP. A case study was conducted as part of this research to validate the proposed framework with 188 DeM components. After the execution of the TPP, Facility Condition Index (FCI) values for the selected buildings were decreased below target value of 0.1, which is considered good condition for the FCI. Therefore, the proposed TPP mitigation strategy for DeM backlogs can help not only in systematically diagnosing the conditions of a building from component to the system level, but can also improve cost-effective budget allocation by improving the FCI value of a building.

Newly emerging (inexperienced) public-private partnership (PPP) markets require private sector participation to change the current situation in which governments are the only entity responsible for funding and supporting infrastructure projects. The decision to implement a PPP approach in an inexperienced market requires identification of potential PPP solutions to mitigate the problems associated with the public delivery of road projects. However, this issue of identification has not been addressed in previous studies. Therefore, the present study aims to develop a framework that identifies potential PPP solutions to public delivery problems. To address this issue, documents that provide first-hand PPP experiences were collected from five toll roads and bridge projects in Senegal, Ghana, Nigeria, Mozambique, and Cote d’Ivoire. Furthermore, the authors conducted interviews with the key stakeholders of toll road projects in Ethiopia—a newly emerging, inexperienced PPP market. The resulting data were analyzed to explore the potential drivers of PPP adoption through a concept-mapping technique, along with case study aggregation. Based on the findings, a framework was developed that consisted of nine PPP-associated factors acting as a solution to 12 public delivery-associated problems. The results demonstrate that fostering economic development with the help of the private sector to raise project financing and constructing roads that can ease traffic congestion are the main potential drivers of PPP implementation in inexperienced PPP markets. This reveals that PPP implementation motives in the inexperienced PPP markets of developing countries are different from the motives in the experienced PPP markets of developing countries. The study findings would help inexperienced PPP markets to find PPP solutions to the problems associated with public delivery.

Formwork is a very important component in the construction industry and can have major impacts on a project’s budget. The process of selecting a suitable formwork system becomes very important in avoiding budget overruns due to choosing an unsuitable system for a project. Despite this importance, the developments for the formwork system are limited to adding accessories to the already existing systems. This was the case until the development of the funicular arch steel truss (FAST) falsework system, which introduced the concept of the funicular arch in developing a new formwork system. The FAST system had some drawbacks that were targeted during the development of the extendable arched steel truss (EAST) falsework system. The EAST system is proven to be more structurally sound than the FAST system and any of the currently commercially available formwork systems in terms of load capacity and satisfying the deflection criteria (span/360). The EAST system also provides higher lateral stability to the formwork system. To understand the advantages of the new EAST system, three case studies were carried out to compare the FAST system, EAST system, and commercially available system, known as the MULTIFLEX girder slab formwork. The results were compared in terms of weight, cost, and erection time. The results proved that the EAST system has a lower erection time than the FAST and the MULTIFLEX girder slab formwork, while it has a higher weight and consequently higher cost than the other two systems; however, it has higher structural soundness and safety factors.

Many industrial projects have experienced significant operability problems and failed to achieve capital project investment objectives during the commissioning and start-up (CSU) phase. To accomplish capital project investment objectives and achieve overall project success, thorough CSU planning, as well as execution of the CSU plan, are critical. However, the importance of CSU planning and execution is often treated lightly. To ensure more effective and successful CSUs for capital facilities, this study examined the correlation between CSU success factor achievement and CSU performance in capital projects, with the empirical data from 26 sample industrial capital projects. To accomplish this research goal, this study (1) developed CSU success factor achievement indicators that can recognize and assess projects’ CSU achievement, and (2) examined the correlation between these CSU success factor achievement indicators and actual CSU performance in capital projects. This study contributes to the body of knowledge in project planning, particularly CSU planning, by (1) identifying 46 CSU success factor achievement indicators, and (2) identifying the relationship between CSU success factor achievement indicators and CSU performance in capital projects. This study contributes to practice by showing how project CSU performance can be improved through the implementation of CSU success factor achievement indicators. Project teams (and their clients) can benefit substantially by paying attention to these CSU success factor achievement indicators.

Unavoidable risks that cause uncertainty within activity durations should be modeled to gain realism in scheduling. While approaches exist for one-dimensional network schedules, two-dimensional linear and repetitive schedules that track both work and time lack such a method. Fuzzy logic can express variability by modeling optimistic, realistic, and pessimistic cases, which, in this study, form a cone of expected activity durations. Previous studies attempted to apply it to linear and repetitive schedules but suffered from inconsistencies that rendered their results incorrect. Therefore, this research proposes a new fuzzy scheduling method. Its contribution is threefold. First, it models linear or segmental activities as continuous yet flexible fuzzy cones. Second, it explicitly expresses continuous buffers in both the time and work/space dimensions to compose an efficient schedule from such inputs. Third, it identifies their full or partial fuzzy criticality and fuzzy float so that managers can split their attention accordingly. It is illustrated by a validation example, which also explains prior inconsistencies. The industry thus gains a new method that considers uncertainty to generate stable and efficient schedules.

One emerging trend in sustainable medium-density construction is the use of mass timber products such as cross laminated timber (CLT), which is a novel approach that involves numerous connectors. Researchers have not previously investigated the potential health impacts of different connectors. This paper proposes a framework to correlate the specification of CLT connectors to the potential risk of exposure to hand arm vibration syndrome (HAVS). We also propose an innovative adaptation of the Location-Based Management System flow line by adding a health risk dimension. The usefulness of the proposed framework is tested using a cutting-edge case study building, the tallest timber building in Scotland. The contribution of this research is a novel appreciation of the impact on installers’ Health & Safety based on the specified type of CLT connectors. With the methodology outlined in this paper, a HAVS variable can be added to design analysis to increase social sustainability in the built environment alongside other sustainability pillars. The findings are relevant to structural engineers, architects, key industry stakeholders, and researchers in the built environment.

Team members in construction projects often fail to communicate effectively, share knowledge, and coordinate as a whole. Although many construction organizations have adopted social media technologies to improve project team processes, little is known about how social media use at work affects project team processes and project management performance. Against this background, employing the enabler-process-intermediate outcome-performance framework, this paper aims to explore the mechanism by which social media use at work affects project management performance with a specific focus on project team processes and project team efficacy. Based on literature research and theoretical analysis, the theoretical model with several research hypotheses is formulated. The empirical data are collected from 156 construction project managers, and the structural equation modeling technique is applied to test the theoretical model. The results show that social media use at work positively influences three team processes, specifically, effective communication, knowledge sharing, and coordination. The three project team processes then indicate different effects on project team efficacy. Furthermore, the project team processes enhance project management performance through project team efficacy. These empirical results reveal the benefits of social media use at work in construction project teams and provide valuable implications for practice.

Robots have increased the safety, productivity, and quality of manufacturing. Recently, sensing, computing, and mapping technologies have started to enable the use of robots in unstructured environments like construction. As robotic construction methods are being prototyped and adopted on site, innovation leaders in construction must analyze the safety, productivity, quality, and cost impacts of the deployment of robots. The researchers gained access to engineering, planning, and production data for the first use of a concrete drilling robot on site. This paper compares the traditional way of drilling holes with the robotic way. Compared to manual drilling for installation hangers, the robot achieved a 10% time reduction, increased task ergonomics by cutting 98% of muscle strain work hours, and reduced rework from 5% to 3%. Our comparison pays particular attention to the three levers a project team has to influence project outcomes—the product, the organization, and the process—and found that decisions like implementing a building information model (BIM) at Level of Development (LOD) 400 facilitated the robot use. This study makes two contributions to the fields of construction robotics and project management: First, this research shares a careful analysis of the application of a drilling robot to offer insights into the applicability of robots on site. Second, this analysis suggests key elements and procedures of a framework to compare robotic and traditional construction methods. Future research should establish the generality of this analysis framework.

In recent years, information technology has been widely used in the construction quality control of hydropower stations, and a series of construction management systems based on the internet of things (IOT) have been produced. However, the existing systems are usually aimed at specific construction quality control factors, leading to the independence of all kinds of monitoring information, which also makes it impossible to directly evaluate the causes of quality problems after they occur. Manual multifactor analysis and evaluation are still needed. Based on a roller compacted concrete (RCC) gravity dam as an example, the construction quality control method is achieved by using WebGIS technology, image recognition technology, voxelization technology, and data mining analysis, which can quickly evaluate the causes of quality problems and integrate all kinds of monitoring information of different orders of magnitude into a presentation scene. This method makes the project quality management process more effective and cooperative. This study contributes to the existing body of knowledge of the quality control method based on informatization in construction.