Journal of Performance of Constructed Facilities

Because bridge modal frequency is inevitably influenced by environmental factors, and single-order frequency is possibly insensitive to some damage conditions, frequency anomaly detection will fail. Therefore, conducting overall multiorder modal frequency anomaly detection is essential considering multiple environmental factors. This paper focuses on identifying multiorder modal frequency anomalies from the perspective of probability. The Gaussian process regression (GPR) model is first established to map the multiple environmental factors to the modal frequencies, whose prediction results consist of frequency estimations with uncertainties. Then, the single- and multiorder modal frequency anomaly characteristic indices are derived by conditional probability and Bayes’ theorem, respectively. Multiorder modal frequency anomaly detection is finally conducted after setting a threshold value. Two groups of GPR models with temperature and environmental inputs are verified in a cable-stayed bridge case. The proposed anomaly detection method can take into account the insensitivity of low-order frequencies to simulated frequency reduction conditions and accurately identify anomalous frequencies affected by multiple environmental factors.

Reinforced concrete (RC) pipe and box culverts are widely used as an alternative to bridge structures in road transport networks around the world. The deterioration of the RC culverts is a complex problem caused by combined humanmade and natural processes with various influential factors. Visual inspection is often used to monitor the deterioration of culverts, and the inspection results are used to rate condition of culverts by using a discrete condition rating system. The objective of this case study was to investigate the deterioration of RC culverts at the network and cohort levels by using a Markov model and culverts’ influential factors and inspected condition data. The Markov deterioration model can forecast the future deterioration of a culvert network, which can be used for asset management planning of the culvert network. A real case study with a regional local government in Australia was used to demonstrate the application of this study. The results of network deterioration modeling showed that the deterioration rates of culverts varied with culvert type (pipe and box culvert), built year, demographic location, and pipe size. However, annual average daily traffic (AADT) affected only box culverts. Deterioration prediction was found to be sensitive to the time length of evidence data, which highlights the importance of keeping records of maintenance and rehabilitation activities for producing accurate modeling data.

The efficient planning of operation and maintenance (O&M) interventions and short-term investments requires adequate and tailored inspection protocols of existing infrastructures. The current study proposes a new standardized inspection system for periodic visual surveys of storage tanks and pumping stations of urban water infrastructures. The proposed system aims at identifying and classifying anomalies in terms of severity, intensity, and extension to evaluate the asset and its components deterioration. Inspection forms specific for water storage tanks and pumping stations are proposed. A set of 22 water storage tanks and 17 wastewater pumping stations located in Portugal were inspected using the developed system. Several anomalies’ interdependences have been identified based on advanced statistical analysis and correlation matrices. Results have also demonstrated that good O&M practices caused slow asset deterioration and increased the asset service life. Main lessons learned concerning the most frequent anomalies, the most frequent distressed components, and the influence of O&M practices in asset deterioration are summarized, allowing a more efficient management of urban water infrastructures. Future research should focus on a wider inspection survey to assess the effectiveness, comprehensiveness, and reliability of this standardized system and to extend it to other urban water assets (e.g., pipes and treatment facilities).

Exposed mass timber is being increasingly used for tall hybrid structures due to its sustainability features, rapid construction time, and the aesthetic desire to see exposed timber. However, there are currently many knowledge gaps in timber’s performance in fire. Current prescriptive methods can be limiting, and designers are therefore required to develop alternative solutions to design tall and/or exposed timber structures. One approach that can be used to better evaluate timber’s performance in fire is numerical modeling, which is often used in synergy with some form of fire testing. The authors have reviewed literature primarily published over the last 5 years to determine the state of the art of modeling timber at elevated temperatures. Following this review, an a priori model of a cross-laminated timber (CLT) ceiling subjected to a localized fire was developed in LS-DYNA to determine what data sets are currently required to better calibrate a thermal model of timber at elevated temperatures. Data sets include the flame spread rate of CLT, the heat flux produced by CLT, charring rates at high heat fluxes, and criteria for the extinction of timber.

Along with the varied climate, topography, and uncertain rainfall, the ever-increasing population has resulted in lowering the groundwater level by 2 m to 3 m in many areas of the Satara district. The present work focuses on investigating the performance of the constructed structures, which are the initiatives toward the conservation of soil and water in the drought prompt areas. The work was divided into three major stages; the initial stage is about data compilation, the second stage is related to the site investigation and collection of data based on the survey requirements, and finally, the interpretation of the obtained data. From the observations obtained through the field survey and ground-level investigations, it is noted that the soil and water conservation works have made a significant impact. From the survey data, it is observed that the change in local crop patterns along with its increased yield and the lifestyle of people residing nearby highlights the usefulness of the various incorporated initiatives. In the drought prompt areas due to the implementation of such schemes, the groundwater table levels significantly increased. But on the other side, several shortfalls were noticed and reported including manual errors, climatic and seasonal reasons, and site selections based on the selected site studies. Overall suggestions for future improvements in the performance of such constructed facilities are presented through this field investigation.

Building operation and maintenance costs are reported to be the longest and most costly phase of a building’s life cycle, where it exceeds costs of design and construction. Although buildings are regularly maintained, water- and energy-saving investments are not often analyzed. This paper presents the development of a novel model for optimizing the selection of upgrade and maintenance interventions for existing buildings to minimize their equivalent annual operation and maintenance cost (EAOMC) while complying with specified annual budgets and building operational performance. The optimization model is developed in three main phases that focus on (1) identifying model decision variables and formulating objective function and constraints; (2) implementing the model computations using binary linear programming; and (3) analyzing the performance of the optimization model using a case study. The primary contributions this research adds to the body of knowledge are (1) a new computationally efficient model for identifying optimal selection of building upgrade and maintenance interventions to minimize EAOMC; (2) modeling reactive, preventive, and predictive maintenance strategies based on component types; (3) integrating a simulation-based upgrade and maintenance approach to evaluate energy and water consumption of buildings; and (4) integrating maintenance and upgrade interventions to maximize economic benefits from building operation by reducing operational and maintenance costs. The results of the case study illustrated the new capabilities of the model in identifying optimal upgrade and maintenance interventions for various operational budgets with up to 32% reduction in EAOMC.

The water diversion tunnel of the Suki-Kinari hydropower station in Pakistan passes through deep valleys with karst caves. Water and mud inrush disasters occurred during tunnel construction because of the adequate water supply from valleys and numerous flow channels. In this work, we develop a karst cave-discrete fracture network model using the C++ code and conduct numerical simulations by ABAQUS version 2018 on a water inrush disaster. Then, considering different strengths and permeability in the reinforced area, we discuss the reinforcement of the fractured rock mass in order to prevent water inrush. Numerical results show that the combination of an increase in strength and one order of magnitude reduction in permeability of fractures is an effective measurement for preventing water inrush. For the specific geological conditions of the A6a diversion tunnel, numerical results indicate that the largest deformation of tunnel offsets toward the orientation of the karst cave. The influence of the relative position of the karst cave varies with cave water pressure, indicating it is essential to consider the relative position of the karst cave and cave water pressure simultaneously for water inrush assessment. In addition, the extent of symmetric deformation is also affected by the relative position between the karst cave and the tunnel.

The design phase is when most critical decisions are made in terms of building maintainability. However, process-oriented empirical studies that identify problem factors and develop strategies remain lacking. Based on this gap in the literature, this research proposes a framework for enhancing architectural design decisions and practices through identifying and exploring problem factors affecting building maintainability. In addition, this research developed strategies to overcome the shortcomings of current practices in the Turkish building sector. Architectural design factors that influence building maintainability and relevant problems were evaluated through a web-based questionnaire survey conducted with facility managers. The findings indicate that improper selection of building materials and inadequate provisions for waterproofing, indoor ventilation, and service areas appear to be prominent areas of concern. Moreover, it was observed that the facility management profession has not gained a well-deserved place in the industry. The strategies proposed to overcome the shortcomings of current practices could be assumed to be supportive actions both for creating awareness in the industry and for making appropriate architectural design decisions about building maintainability.

Urban road collapse seriously threatens people’s lives and property. In recent years, with the accelerating pace of urbanization in China, the number of road collapse accidents (RCAs) has been increasing. Based on 334 RCAs in China’s inland cities from 2019 to 2020, this paper divided the severity of urban road collapse into five grades and analyzed the causes, risks, and countermeasures of urban road collapse. The results indicated that causes of RCAs include rainfall, underground pipe problems, construction in progress, geological environment, road aging, road overload, and unqualified backfilling. Rainfall and underground pipe problems cause 75% of RCAs, and the risk of RCAs caused by underground pipe problems is the greatest. Its calculated value is 6.13, which is 3.2 times that of construction in progress and 22 times that of unqualified backfilling. Construction in progress and geological environment problems are more likely to lead to RCAs with higher severity. Underground pipe problems, rainfall, construction in progress, and geological environmental problems are all more likely to cause RCAs in summer, resulting in 45.9% of RCAs occurring in summer. The risk of RCAs in June is the highest, and its calculated value is 1.5 times that in September and 25 times that in February. According to the calculated values and geographical location factors, China was divided into five regions. Finally, the countermeasures before and after road collapse were introduced.

Quantitative safety assessment of offshore platforms under ship collision is a hot topic. Numerical simulation is a powerful tool to evaluate the damage and failure mode of offshore structures under collision. This paper incorporates an element-coupling method into the simulation of ship-platform collision, in which the jacket platform is established using different elements to reduce the modelling costs of the traditional pure shell element modeling method. The coupling method for connecting the interfaces of different elements is proposed and validated through a case study. Further, numerical simulations are carried out for different collision scenarios with collision velocity from 1 to $5\mathrm{m}/\mathrm{s}$. According to the simulation results, three different types of failure modes of the jacket are identified namely as “Local denting,” “Brace buckling,” and “K-joint rupture” corresponding to low-energy collision, medium-energy collision, and high-energy collision scenarios, respectively. Also, the detailed failure mechanism of the collision area under a typical high-energy collision is assessed. The collision process is divided into six stages according to the analysis of collision force results. The safety of the jacket platform under collision is assessed through the overall results of vibration acceleration and horizontal displacement. The work in this paper can greatly reduce the modeling efforts and calculating costs, and is thus suitable for conducting numerical analysis of failure mode and collision process in a more efficient way.

Bridge modal frequency is an important parameter reflecting its overall property change and widely used for bridge condition assessment. However, the effects of multiple environmental conditions on the modal frequency will mask the variation induced by structural damage. Traditional single regression models cannot quantify measurable and unmeasurable environmental effects simultaneously, resulting in poor prediction and separation performance. Therefore, a two-stage elimination model (TSEM) integrating regression analysis and trend decomposition technique was developed. Environmental principal components (PCs) sensitive to the single-order modal frequency were extracted based on partial least-squares analysis. To quantify the nonlinear effects of measurable environmental factors, the baseline predictor with respect to modal frequency and environmental PCs was constructed through relevance vector machine technology. An error compensation model based on singular spectrum analysis was established to extract trend-related components and remove the part of residual modal variability unknot considered by the baseline model. On this basis, exponential weighted moving average control chart was established to highlight slight abnormal changes in modal frequency. A cable-stayed bridge case verified its validity and accuracy. The results indicate that the proposed TSEM has better modeling, generalization, and separation performance than the baseline model, and the variation of normalized frequency tends to be more stable. Additionally, the significant differences of damage sensitivity of different orders were determined.

Precise documentation of as-is status of transportation infrastructures is an essential task for several public transportation agencies. To tackle this task, such agencies have adopted various advanced technologies, such as light detection and ranging (LiDAR), for three-dimensional (3D) reconstruction and as-built documentation purposes. In this research, we study the feasibility of an alternative 3D reconstruction method, photogrammetry, which has the advantage of being inexpensive and easy to operate compared to LiDAR. To assess the proposed alternative method in transportation asset-inventory collection, this paper evaluates the feasibility of using photogrammetry in providing an acceptable 3D point cloud model of two important categories of transportation assets: roadway assets and pedestrian access ramps. The analysis of the data quality and associated cost attests to the feasibility of using close-range photogrammetry in pedestrian access ramps inventory, while using this technology as a complementary tool with LiDAR in the mobile setting holds the promise for a feasible roadway asset data collection.

Computer vision based on machine learning theory has been widely used in the surface damage detection of concrete structures, but the characterization of internal damage in concrete still remains a challenge for researchers. Aiming at this problem, we propose a nondestructive evaluation (NDE) method to classify diverse conditions of internal damage in concrete based on short-time Fourier transform (STFT) and convolutional neural networks (CNN). The STFT converts the self-resonant vibration signals into two-dimensional time-frequency images that can be used as the input data for the CNN. The training set is fed into the CNN for feature extraction and classification, and then the testing set is brought into the trained model for verification. Both a simple case of virgin state and damaged state, as well as a complicated case covering all four internal damage states were successfully classified with an excellent recognition rate of testing samples. The key CNN hyperparameters were optimized and the classification accuracy rate of spectrum images was as high as 98.8%. Optimal data set size was also found to balance the accuracy and efficiency. The findings in this work validate the feasibility of the CNN for the detection and differentiation of invisible damage in concrete nondestructively.

In recent years, more and more disasters caused by the fracture of bridge cables have been reported. Brittle fracture of cables is mainly caused by corrosion fatigue, and thus it is crucial to find cable defects early. The present study developed a cable inspection method embedded with a lightweight deep-learning model and equipped with micromagnetic sensors, based on intelligent image recognition and magnetic memory technology. After four cable-stayed bridges were found with defects, five types of defects and features on the surface of cables were identified by the SqueezeNet network model with the image denoising algorithm and transfer-learning method, with accuracy of 97.18%. The corrosion along the cable was positioned with micromagnetic sensors. Four alerting levels were proposed and corresponding remedial measures were suggested to be implemented. The novelty of this work lies in the intelligent detection of bridge defects, as well as accurate evaluation of long-term performance of bridge cables.

Abstract

An 11-story high-rise building with a shear-wall structure utilized a raft foundation based on a deep alluvial deposit. After structure completion, the building tilted beyond acceptable limits. This paper presents the investigation, settlement analysis, building rectification, and foundation remediation. The purpose of this study is to provide a common method of addressing similar problems and propose two formulas to guide design. First, through site investigation and settlement reanalysis, it was determined that the large settlement and building eccentricity accounted for the building tilt. Second, underexcavation was used for the side with less subsidence to address the building differential settlement, and precast micropiles were driven into the foundation to improve foundation safety. Finally, two formulas for underexcavation were proposed to apply to general rectification projects. A simplified analysis method was adopted for formula derivation. In accordance with the force balance in the foundation soil before and after underexcavation, the preferable hole spacing could be expressed as a multiple of the hole diameter, i.e., $\lambda =K/(K-1)$, which mainly depends on the safety factor of the bearing capacity of the foundation soil. Assuming that the extraction volume of soil equals the forced settlement volume for inclination correction, the diameter of the hole is obtained, i.e., $d=4\lambda s/\pi$, which mainly depends on the proposed forced settlement. The key parameters were verified by the case history. The case history and practical design method provide a framework for projects experiencing differential settlement.

Practical Applications

Buildings sometimes incline. Reasons for the inclination are various, such as severe foundation settlement, surrounding underground construction, neighboring foundation pit excavation, and underground water drainage. Underexcavation is a cost-effective and “soft” method of rectifying buildings. It is suitable for buildings with shallow foundations. This paper presents a case history, and theoretical formulas for underexcavation design are proposed. In the case study, the methods of investigating and analyzing building inclination and the technology of inclination correction can provide valuable references for similar projects. The theoretical formulas can provide the key parameters of limit hole spacing and hole diameter for a correction design and enable a designer to quickly give a correction scheme for general conditions. For specific projects, appropriate adjustments should be made according to geotechnical conditions, the surrounding environment, and the suitability of construction. Notably, even with the formulas to guide design and construction, in view of the complexity of geological issues, close monitoring is still essential.

Concrete pavers and slabs commonly are found in a variety of community settings, such as footpaths and walkways. However, their features of slip resistance have not been investigated meticulously for the prevention of slip and fall incidents. This preliminary study assessed the traction functioning of dissimilar finishes of concrete surfaces and determined their impact on slip resistance. For the preparation of concrete slab samples, three generally practiced approaches—metal and wood blade troweling, and bloom-sweeping—were utilized to create dissimilar topographic structures. Slip resistance performance was assessed using a dynamic friction test rig developed to simulate ambulatory movements between the concrete flooring and shoe samples over four different levels of risky conditions. Assessment findings showed that the measured concrete slab–shoe arrangements presented excellent slip resistance in dry conditions. A substantial impact of the concrete slabs’ topographic structures was uncovered in moderately (soapy) and highly (oily) dangerous surroundings. However, the shoe effect was rather insignificant than the slab one on dynamic friction coefficients. This study also discovered that concrete floors with rougher textures did not routinely provide better traction even in contaminated situations. On the other hand, this study identified crucial proof of friction and friction-provoked wear developments of concrete floorings and their effects on slip resistance performance. Outcomes from this study may be used to improve surface designs of the concrete footpaths and/or pavements to preclude slip and fall incidents in the community.

Reinforced concrete (RC) structures with weak or soft stories have exhibited poor seismic performance in several past earthquakes, primarily due to high seismic force and deformation demands on the columns in these soft stories. This study presents a strategy to improve the seismic performance of these structures by using structural steel–reinforced concrete (SRC) columns. A design methodology using the energy-balance concept was proposed for the RC-SRC hybrid structures. A five-story study frame with conventional RC as well as SRC columns was numerically modelled in the computer software OpenSees to assess seismic collapse performance. The nonlinear modelling technique was validated by comparing the predicted cyclic force-deformation behavior with the past test results. Nonlinear cyclic pushover and dynamic time-history analyses were conducted to compare the seismic performance of the RC and SRC frames. Incremental dynamic analyses (IDAs) were conducted to predict their collapse probabilities and derive their collapse fragility curves. The results of the static and dynamic analysis show a significant improvement in the seismic performance of the hybrid frame over the original RC frame.

After hazard events, large numbers of images are collected by reconnaissance teams to document the post-event state of structures, and to assess their performance and improve design procedures and codes. The majority of these data are captured as images and manually labeled. This highly repetitive task requires considerable domain expertise and time. Advances in deep learning have enabled researchers to rapidly classify reconnaissance images. Thus far, these classification methods are limited to a simple classification schema in which the classes are all either mutually exclusive or independent. To date, an efficient classification system of a complex schema containing many classes arranged in a multi-level hierarchical structure is not available to support earthquake reconnaissance. To address this gap, this paper introduces a comprehensive classification schema and a multi-output deep convolutional neural network (DCNN) model for rapid postearthquake image classification. In contrast to past work, herein a single multi-output DCNN classification model with a hierarchy-aware prediction was trained to enable the rapid organization of images. The performance of the proposed multi-output model was validated through comparisons with multi-label and multi-class models using an F1-score. As result, the multi-output model outperformed other models. Then, the multi-output model was deployed to a web-based platform called the Automated Reconnaissance Image Organizer, which can be used to easily organize earthquake reconnaissance images.

This case study focuses on excessive and undesired deformations that occurred during construction of newly rehabilitated steel bridges. The case study responds to problems with elevation control and subsequent ride quality problems exhibited in newly constructed or newly rehabilitated steel girder bridges made composite with concrete decks cast atop steel girders. Forensic investigations were performed on three bridges that had been in service for only a few years. These investigations discovered strong evidence that cantilevered portions of concrete decks were not supported adequately during construction loadings. The purpose of the case study is to identify causes for undesirable elevation profiles, and to recommend design and construction practices to help mitigate problems created for the highway users and extend the useful life of bridges.