Journal of Performance of Constructed Facilities

This paper summarizes the blast events and resulting damage found on more than 100 bridges in Iraq caused by terrorist attacks. Most structures were two to three lanes wide and composed of a superstructure with prestressed concrete beams that supported a concrete deck. In most cases, terrorists placed charges either on the deck or at the base of columns. Typical structural system failures were due to either intermediate column failure, girder end failure, or girder end failure in combination with midspan failure. It was found that a single blast load on the deck rarely caused collapse, while three or more blast loads distributed across the deck nearly always caused deck and/or span collapse. If at least two girders remained largely undamaged, one lane of traffic could often be directed safely over the structure, allowing a heavily damaged bridge to remain in temporary use. When columns were attacked, charges placed symmetrically caused the columns to collapse downward, whereas charges placed asymmetrically caused significant lateral movement. Although most bridges were simple span, substantial resiliency was observed, where the structures could support large amounts of load even when some critical components theoretically had near-zero capacity. It was further found that separating components, even slightly, could significantly reduce blast damage propagation. Brief recommendations are given to mitigate blast damage.

Structural health monitoring (SHM) data encompasses vital information that provides a comprehensive understanding of the structural health condition. However, data loss may occur due to faults in acquisition equipment or sensors, and it is essential to reconstruct missing data to ensure the integrity of the monitoring information. Although extensive researches have been conducted on the topic of data recovery, a suitable missing data recovery method that can effectively address the missing data for multiple-source monitoring variables has not been identified yet. In this study, we proposed a novel missing data recovery model based on a deep learning framework to recover the missing strain and acceleration data simultaneously for SHM of the quayside container crane (QCC). The framework combines dual-tree complex wave transform (DTCWT) and bidirectional long short-term memory with attention mechanism (Att-BiLSTM). The multiple-source monitoring data are decomposed into several subtime series using the dual-tree complex wavelet, then, the Att-BiLSTM network is used to assign different weights to each subsequence in order to capture valuable information from the complete data. The effectiveness of the proposed model is verified by case studies, and the comparison results of missing data recovery under different miss rates show that the proposed model simultaneously improves the accuracy of missing data recovery for multiple-source monitoring variables.

Cross-laminated timber (CLT) is a mass timber product that has been used increasingly as a sustainable and cost-effective alternative to conventional construction materials such as concrete and steel. Understanding the seismic performance of CLT buildings is vital in the design process for engineers as the use of CLT has increased toward mid- and high-rise buildings. In this study, the experimental dynamic behavior of three full-scale two-story platform CLT shear wall mock-ups were investigated under quasi-static reversed cyclic and interval impact hammer modal tests. The mock-ups differed in the tension splice between the two stories, the level of dead load, and the installation of an acoustic insulation layer between stories. The impact hammer modal tests were conducted during different steps of the reversed cyclic tests to extract modal characteristics such as natural frequencies, damping ratios, and mode shapes in the defined drifts of frequent, medium, and rare seismic hazard levels. The mock-up’s experimental first period was in good agreement with the results predicted by the empirical equation in the National Building Code of Canada (NBCC). The stiffness degradation during the reversed cyclic tests was related to frequency reductions representing an index of damage. The presence of an acoustic layer increased the damping ratio and stiffness in the first three measured modes. However, further investigations are needed to see the extent of the effect of acoustic layers between the walls and floors on the lateral performance of timber buildings equipped with CLT shear walls and floors.

Automatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable.

Serious water inrush accidents occur frequently in the fault-prone area around Hongtu Tunnel, which encompasses several reservoirs and large bodies of water. In this study, a field test was used to identify the water inrush mechanism and the design characteristics of the tunnel’s drainage system, the impact of water gushing through the tunnel on the surrounding environment was analyzed, and the results were used effectively to guide the design and construction of a supporting engineering drainage system. The specific conclusions are as follows. The supply source of the water causing the inrush accident was far away from the tunnel. The groundwater flowed to the site of the burst through a groundwater network connecting a fault and rock fracture. Full-section curtain grouting reinforcement with a thin grouting ring was carried out first to meet the construction requirements of the tunnel face before further excavation in the water-rich fault zone, followed by postgrouting of the weak section after blasting for further construction of the new roadway to strengthen the water-blocking capacity of the excavated part. The use of curtain grouting and postgrouting together reduced the disruption to construction near the tunnel face and thereby sped up the new construction. The seepage pressure calculated using the equivalent penetration coefficient method was weaker than the water pressure borne by the support structure in the middle of the two rows of the annular blind pipes, which was an important reason the support structure cracked during the operational period; thus, a higher safety factor should be considered when using the equivalent permeability coefficient. These research results can serve as a useful reference for the design and construction of drainage systems in similar cases.

Abstract

During the last 20 years, fluid-related natural catastrophes caused by climate change have produced severe floods in numerous countries, resulting in many casualties, large scale infrastructure damage, and enormous economic losses. These catastrophic hydrodynamic phenomena disrupt whole transportation networks by washing down bridge decks, piers, and roadways, complicating rescue and recovery efforts. As recent flash floods have demonstrated, inland transportation infrastructure is just as vulnerable to fluid hazards as coastal infrastructure. This research investigates the mechanics of fluid flow impact to learn how fluid currents affect bridge piers. The next step is to build a finite volume bridge pier model that considers the consequences of fluid flow and radial motion around the pier. Results from fluid impact analysis, fluid–structure coupling analysis, and theoretical analysis are contrasted with those derived using the equations specified by national and international design standards. According to the results, it is necessary to raise the fluid flow force computed using the codes’ formulas to account for the impact of the flood on the bridge pier. The standard codes for the highway bridge design approach frequently produce a larger fluid flow force result, so we can ignore fluid–structure interaction on the bridge pier in water flow velocity, which is minor. It is possible to disregard the fluid–structure interaction on the bridge pier only when finite volume analysis is performed.

Practical Applications

Understanding the impact of fluid flow on bridge piers is crucial for enhancing the resilience of transportation infrastructure in the face of increasing hydrodynamic threats due to climate change. This research delves into the mechanics of fluid–structure interaction, shedding light on how fluid currents affect bridge stability. By developing a finite volume bridge pier model, the study provides a more accurate assessment of the forces exerted by floods, enabling engineers to design bridges that can withstand extreme hydrological events more effectively. The findings highlight the limitations of current design standards, suggesting that traditional approaches may underestimate the forces exerted by floodwaters on bridge piers. By incorporating fluid–structure interaction analysis into design protocols, engineers can ensure safer and more resilient bridge infrastructure, reducing the risk of catastrophic failure during severe flooding events. This research underscores the importance of adopting advanced modeling techniques to account for dynamic fluid behavior and mitigate the impact of hydrological pressures on critical infrastructure.

Transportation networks in the United States contain more than 615,000 bridges. Federal regulations require routine inspections of these infrastructure systems at least every 24 months. However, recent studies have suggested that this approach is costly, inefficient, and, in some cases, risky. An essential element in developing customizable alternatives for condition assessment plans is the design of precise and robust forecasting models for bridge deterioration, capable of sustaining acceptable predictive accuracy even when operating with small subsets of data. The overarching objective of this research is to introduce a novel forecasting method for bridge deterioration conditions, augmented with an exploration mechanism to identify a subset of bridges crucial for maintaining the predictive power of the model in subsequent years. The proposed method comprises two main components. Firstly, a forecasting model for bridge deterioration conditions was designed, formulated, and developed based on a novel framework for ordinal extreme gradient boosting. Secondly, an exploration mechanism is employed using a new multidimensional clustering method that integrates K-means clustering and dynamic time wrapping. The proposed method is applied using historical condition assessment data from bridges in the three neighboring states of New York, New Jersey, and Connecticut. The outcomes validate the performance of the proposed method, demonstrating that the forecasting model accurately predicts future bridge conditions. Additionally, the clustering component successfully identifies a small subset of bridges essential for maintaining the predictive power of the forecasting model. The findings of this study will assist transportation agencies in utilizing their bridge inspection resources more efficiently and in customizing their condition assessment operations based on bridge characteristics and expected deterioration levels.

Abstract

The inspection of building components plays a crucial role in reducing maintenance costs throughout the building’s life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls.

Practical Applications

This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management.

It is well known that many residential construction projects suffer from low quality due to a variety of factors; therefore, to find out the level of dissatisfaction among stakeholders, the underlying causes, and the recommendations, this study uses a survey. Owners, controllers, and contractors were asked to complete questionnaires online. To rank dissatisfaction levels, incomplete or defective works, and suggestions for improving construction quality, data from the survey were analyzed by frequency indexes (FIs), and project stakeholders’ perceptions were aggregated by “probabilistic or.” The study reveals that 52% of flat owners are somewhat satisfied with the building inspection company’s inspections, but most owners agreed ($4.13\pm 0.98$) that consultants should inspect the construction, whereas the lowest dissatisfaction ($1.71\pm 1.30$) is “getting smaller apartments than promised.” Construction dissatisfaction can be attributed primarily to “high-profit expectations by building contractors,” whereas “the lack of sufficient qualified workers,” “the lack of competence of building contractors,” and “incompetence and ineffectiveness of site chiefs/managers” are also prominent. From the perspective of controllers and contractors, the highest consensus ($4.43\pm 0.92$; $4.24\pm 0.85$) is “needing to work with experienced construction site managers,” whereas the lowest dissatisfaction ($2.57\pm 0.72$; $2.45\pm 1.02$) is “the overall quality of constructed residential buildings.” It appears that contractors’ dissatisfaction is lowest ($2.42\pm 0.95$) when it comes to “contract-conforming construction quality.” A general dissatisfaction rate of close to average is reported by the owners, whereas the dissatisfaction rates of the controllers and contractors are close to “above average.” Based on the findings, this study provides valuable information and recommendations for improving the quality of construction for the successful completion of building projects.