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In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The k -means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

One of the key tasks to ensure infrastructure safety is the periodic detection of bridge cracks. Since manual crack detection is subjective and inefficient, it is very important to develop an automatic crack recognition system by using machine vision. Inspired by the pyramid vision transformer (PVT) and the feature pyramid network (FPN) variants, a method combining PVT, residual transformer (REST), holistically nested edge detection (HED), and downstream detection tasks is proposed in this paper, which is named EVPTMFF (efficient visual pyramid transformer and multiple-feature fusion). Based on the PVT, the multiheaded attention module was replaced and the efficient attention module was adopted, which could process the data efficiently and flexibly. To improve the performance of EVPTMFF, the original perceptual field windows were changed. The adjacent windows were partially overlapped, which was more conducive to feature interaction and improves detection performance. To prove the generalization ability of the model, three different data sets related to bridges were collected and formed. We carried out experiments on these three data sets, and EVPTMFF showed good results. Especially for larger data sets, the performance advantage was more significant. Practical Applications: The crack detection model proposed in this paper presents a good detection effect under different illumination and interference. And the detection results, collected data, time, and other information are saved to the bridge crack detection software. This can help engineers quickly and accurately detect cracks on the bridge surface, as well as predict the development trend of cracks and possible safety issues. In practical application, the bridge crack detection system can help engineers find and solve the bridge crack problem in time and avoid the security risks and economic losses caused by cracks. At the same time, the efficiency and accuracy of bridge maintenance can be improved, and the maintenance cost and time can be reduced. The bridge crack detection system can be integrated with other hardware equipment and management systems to form a complete bridge management platform, which contributes to the traffic construction and social and economic development of the city. [ABSTRACT FROM AUTHOR]

The detection and evaluation of internal corrosion of concrete bridge decks is of critical importance for bridge structure safety. Ground penetrating radar (GPR) is a nondestructive testing technique that has been used widely for corrosion assessment of RC bridge decks. However, the recognition accuracy of traditional automatic data processing methods for blurred hyperbolic features is insufficient, which leads to a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic rebar picking algorithm based on gradient information and a migration method for bridge deck corrosion assessment with GPR data. This method can be divided into three steps: thresholding processing based on B-scan gradient information, a limited hyperbolic summation-based recognition (LHSR) algorithm, and rebar localization. First, the GPR B-scan is transformed into its gradient image and thresholded to enhance hyperbolic features. The LHSR algorithm then is applied to the binarized gradient B-scan to identify the hyperbolas, locate the rebar, and extract the rebar reflection amplitude. Finally, the corrosion map of the bridge deck is generated based on the rebar position and the rebar reflection amplitude after depth-correction. A case study with GPR data from two tested bridges was employed to validate the feasibility of the proposed method. The results show that the precision and recall of automatic rebar picking by this method for poor-quality GPR data were 91.27% and 93.56%, which are significantly higher than those of the traditional methods. Moreover, the accuracy of the bridge deck corrosion map obtained by the proposed method also is significantly better than that of the traditional methods. It can be concluded that the proposed method can be used for rebar picking and corrosion assessment of RC bridge decks with GPR data. Ground penetrating radar is a nondestructive testing technology that has been used widely for corrosion assessment of concrete bridge decks. However, the traditional automatic processing method of GPR data has insufficient recognition accuracy for blurred features of rebars, resulting in a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic method that can be used for feature identification and location of rebars. The corrosion map generated by the position and reflection amplitude of rebars could help engineers to conduct corrosion assessment and repair of concrete bridge decks. The feasibility of the proposed method was validated through a case study with GPR data form two actual bridges. Especially for poor-quality GPR data, the accuracy of rebar picking and corrosion maps of this method is significantly higher than that of traditional methods. It can be concluded that the proposed method can be used for rebar positioning and corrosion assessment of concrete bridge decks based on GPR inspection data. [ABSTRACT FROM AUTHOR]

As the lifeblood of national energy, oil and gas pipelines are vulnerable to corrosion, third-party damage, and natural disasters. Among these, third-party damage accidents have a high probability and serious consequences. However, China lacks a mature and unified pipeline accident database, making it difficult to assess and manage the risk of such incidents. To address this issue, this paper identifies 20 key risk factors for third-party damage to oil and gas pipelines through literature and case studies. The paper then proposes a multistate probabilistic assessment model using the Decision-Making Trial and Evaluation Laboratory (DEMATEL), interpretative structural modeling method (ISM), Røed method, and Bayesian network (BN). Real case studies demonstrate that the model not only predicts the probability of third-party damage risks but also clearly identifies key influencing factors and causal chains in the risk system. This provides a new approach for assessing third-party damage risks to oil and gas pipelines and offers a scientific basis for decision-making in managing actual pipelines. Practical Applications: The high probability and serious consequences of third-party damage accidents of oil and gas pipelines have caused certain hidden dangers to social security, environment, and economy, and put forward higher requirements for pipeline operation and management. In order to scientifically manage and effectively control the occurrence of third-party damage accidents of oil and gas pipelines, this paper, in the absence of an applicable pipeline failure database. Based on expert knowledge, a model that can be used for the assessment and prediction of third-party damage probability of oil and gas pipelines is constructed. Through the application of real cases, it is shown that the model can be used not only to predict the probability of third-party damage of oil and gas pipelines, but also to identify the key influencing factors and key causal chains leading to third-party damage. The results of the study not only provide theoretical support for pipeline management in reality, but also provide new ideas for risk assessment of oil and gas pipelines that lack actual data. [ABSTRACT FROM AUTHOR]

Segmental tunnel linings have a complex structural behavior that is difficult to predict. Full-scale in situ structural monitoring allows us to gain access to these structures, observe the acting mechanisms, and better understand the actual structural behavior under realistic boundary conditions. In a recent structural monitoring project in Frankfurt, Germany, strains, deformations, and temperatures were measured at the segmental lining of a twin-tube tunnel boring machine (TBM) tunnel. Hereby, internal forces were back-calculated from the strain measurements. The results of the monitoring are presented and discussed in this paper. The measurements began immediately after the assembly of the segments and have been performed continuously over several years. Because of that, it was possible to observe that the internal forces differ significantly between the construction stage and the final state. During the construction stage, the internal forces develop quickly within hours after ring assembly, showing a distribution along the lining's circumference that is hard to predict. Almost all radial deformations occur during this period and remain unchanged afterward. After this first early phase, a period of stress redistribution follows, including significant changes in the internal forces. At its end, the stable, final state is reached. In this state, the internal forces match better with expectations. In the monitored areas, the stresses during the construction stage slightly exceeded those of the final state, which should be avoided for economic reasons. Additional investigations regard the temperature development within the lining, the influence of the second tube construction on the already built tube, and the development of longitudinal ring interaction over time. The latter significantly influences the structural behavior, but its development is often subject to uncertainties. In this case, it was observed that the longitudinal interaction decreases considerably over time. Practical Applications: In the structural design of segmental tunnel linings, structural or geotechnical engineers have various options regarding the available calculation methods and modeling strategies. All of these options are part of the state of the art, and the selection is up to the engineer. However, the results can be quite different depending on the chosen option. Consequently, uncertainties remain regarding the real linings' internal forces and structural reliability. The actual mechanisms can be identified by investigating the real structural behavior of segmental tunnel linings with in situ structural monitoring. Then, with back-analyses, on the one hand, it can be tested to determine how well different calculation methods and modeling strategies correspond to reality. On the other hand, the monitoring data can contribute to improving existing calculation methods, developing new modeling approaches, and establishing more detailed recommendations or optimizations regarding the design and construction of segmental tunnel linings. This paper presents findings derived from a structural monitoring project on a segmental lining. Different mechanisms and aspects, sometimes uncertain or debatable in tunnel design, are discussed regarding their real in situ appearance. The gained data will also be used for further back-analyses and investigations. [ABSTRACT FROM AUTHOR]

Crack detection methods of high-rise building walls based on traditional computer vision (CV) heavily rely on manual selection and extraction of design features. Convolutional neural network (CNN)-based CV can actively learn the features of cracks and adapt to complex backgrounds, solving the limitations of traditional crack detection methods. This paper explores faster region-CNN, single shot multibox detector (SSD), You Only Look Once for crack detection, and Mask R-CNN for crack segmentation and proposes a novel automatic crack geometric quantification method by combining CNN-based object detection and segmentation. The contents include (1) crack detection and bounding box extraction, exploring a variety of models, selecting the best model to detect the image taken by an unmanned aerial vehicle (UAV), and extracting the crack region; (2) crack segmentation, using the detection results of the first part as input for more accurate detection and segmentation of cracks; and (3) a novel pixel-level geometric quantization method of crack based on Hough straight-line detection, mainly including crack length and width. Then, the pixel level is transformed into the actual geometric quantization to simply determine the crack severity. The three models generated in these three parts can be used for managing exterior wall cracks in high-rise buildings for different inspection purposes. [ABSTRACT FROM AUTHOR]

Limited research has been directed toward coastal protection infrastructure compared to other types of infrastructure, despite the increasing global population in low-elevated coastal regions and the threats posed by climate change. This paper presents a risk-based asset management framework for coastal protection structures that improves accuracy with each inspection. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA) module, Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). The framework addresses challenges in accurately predicting coastal structure deterioration due to uncertainties in wave loading conditions and the need for frequent inspections. It is applied to rubble-mound breakwaters in Alexandria, Egypt. The BMDM and FMDM models are developed based on inspection data, and the IPE optimizes interventions considering structural condition, risk thresholds, and budget constraints. Results showed that long-term deterioration estimates occur at an accelerated rate with an increase in inspection points, triggering earlier interventions. However, the framework proves reliable even with only two inspection points, allowing asset managing agencies to implement the model based on the structural condition at the year of construction and a minimum of two inspections. The proposed risk-based asset management framework provided a comprehensive approach to managing coastal protection infrastructure, reducing risks to life and property. By accurately predicting deterioration and optimizing intervention decisions, the framework can greatly assist in the effective management and maintenance of coastal assets. This is vital in ensuring the safety of coastal populations facing global climate change and demographic growth. Coastal protection structures are crucial for safeguarding coastal populations in the face of climate change and demographic growth. This research paper introduces a risk-based asset management framework for coastal protection structures, with practical applications for coastal management agencies and practitioners. The framework addresses the challenges of predicting coastal structure deterioration and optimizing intervention decisions. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA), Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). It has been successfully applied to rubble-mound breakwaters in Alexandria, Egypt. The practical implications are significant: The framework enables asset managers to make informed decisions on coastal infrastructure maintenance and interventions, reducing risks to life and property. It provides a comprehensive approach to managing coastal protection infrastructure and ensuring the safety of coastal populations. By accurately predicting deterioration and optimizing interventions, the framework supports effective management and maintenance of coastal assets. Its reliability and flexibility make it a valuable tool for coastal management practitioners, promoting sustainable development and safeguarding coastal areas. With its potential for broader application, this risk-based asset management framework offers practical solutions for coastal protection worldwide. [ABSTRACT FROM AUTHOR]

This paper discusses the development of ultrasonic stress measurement (USM) technology as a potential tool for structural heath monitoring and evaluation of buildings and bridges. The proposed approach utilizes acoustic birefringence to assess the total stress in steel members. Acoustic birefringence describes the difference in velocity between orthogonally polarized shear waves as a result of anisotropy in the material. This anisotropy comes from texture produced during the manufacturing process and stresses (i.e., strains) in the plate under loading. The relationship between the birefringence and stresses may be a viable method to evaluate the total stress in a steel member, which includes stresses produced from all applied loads, including both dead loads and live loads. This paper investigates steel wide-flange (W-flange) rolled sections under tension and bending. The results indicate that birefringence shows a strong linear correlation with strain. The variation in the in situ birefringence was found to be significant and varied between different locations on the same cross section. If the acoustic constants (B0 and m1) that relate the birefringence to the strain are known, laboratory tests showed that stress can be approximated to within ±1.1 ksi (±7.6 MPa) (2% of the material yield strength, Fy). If average values of constants across all laboratory testing were used, the error increased to ±14.79 ksi (±102.0 MPa) (28% Fy). These results indicate that ultrasonic stress measurement based on acoustic birefringence measurements may be a viable method for the evaluation of total stress in steel members but needs additional research to identify and mitigate the sources of variation in critical acoustic constants that affect the accuracy of the approach. [ABSTRACT FROM AUTHOR]

This paper focuses on concrete degradation of hydraulic conveyance components of a hydropower project in Uganda that was commissioned in 2019. During a routine outage inspection in 2021, concrete damage was observed in the waterways. The engineering-procure-construct (EPC) contractor was notified to remedy the defects but they declined, claiming that it was normal erosion wear. In order to ascertain the root cause, avert a potential dispute, and apportion liability, a forensic investigation was conducted by the owner. Site inspections, water quality sampling and testing, and concrete core sampling and testing were carried out during the forensic investigation. The findings indicate that the Nile River water is highly aggressive, and the coupled soft water attack and hydraulic erosion-abrasion led to the premature concrete surface degradation. In order to enhance durability and long-term performance of the waterways, a protective surface coating has been recommended. The investigations have been very useful to avert costly and time-consuming disputes between the owner and the contractor because the cause and party responsible for the defects have been identified, and the contractor has agreed to take remedial action in accordance with the contract. The study will be useful for other planned hydropower projects along the Nile River and similar projects globally. The paper reveals the significance of forensic investigations at a hydropower infrastructure project to ascertain the durability of the civil engineering components of the facility, and in turn the overall performance of the infrastructure. The study undertakes a forensic investigation of concrete degradation within hydraulic conveyance components of a recently commissioned hydropower project in Uganda. The findings reveal practical insights for industry practitioners on the key design and construction considerations for hydropower plants or similar water conveyance systems. Key findings from the investigations, as well as durability recommendations for future project waterways, are shared for best industry practice, including adopting a comprehensive durability design and performance-based approach for key infrastructure such as hydropower plants. [ABSTRACT FROM AUTHOR]

Increasing demand for railway transportation combined with more severe climate events, such as extreme heat, leads to an increased risk of degradation of track support and failure due to rail buckling. In this paper, distributed fiber optic sensing (DFOS) was used, for the first time, to assess track support degradation and the likelihood of rail dynamic buckling of curved rail sections. A monitoring campaign was conducted to measure the dynamic distributed strain response of a 9-m-long section of curved track during the passage of a passenger train. The distributed strain data were used to assess the axial strain and vertical bending curvature response during the passage of the train, and the distributed vertical curvature profile was then used to evaluate the wheel forces and track modulus of the monitored site using the Bayesian inference approach. The estimated wheel forces showed good agreement with the expected values, and the estimated track modulus was comparable to that measured using conventional techniques at other similar rail sites. With the estimated wheel forces and track modulus as inputs, a finite-element model developed in a commercial software package (i.e., ABAQUS) was used to assess the dynamic buckling capacity of the rail by considering the reduced rail lateral resistance due to the passing train. The results indicate that for this site, the passage of locomotives reduces the thermal buckling capacity by several degrees Celsius depending on the initial geometric imperfections in the rail, whereas passenger cars have negligible impact on the capacity. [ABSTRACT FROM AUTHOR]

Optimal facility repair planning is a challenge, especially as it applies to a portfolio of facilities constrained by a limited budget. This paper discusses a linear programming optimization method to develop improved maintenance and repair strategies. This method introduces a utility metric, termed component "endurance," used to determine repair decisions to help maximize the financial health of a portfolio of facilities. A predictive model is used to calculate this endurance metric to measure the impact of repair decisions at a specified future date. This future impact is integrated into an optimization framework to guide repair decisions with the aim of avoiding excessive deterioration of the most valuable components while remaining within budget. This approach shows appreciable benefits over traditional component condition ranking approaches, including increased portfolio health and reductions in deferred maintenance. [ABSTRACT FROM AUTHOR]

Multistory industrial buildings have become prevalent in sectors such as electronics, machinery, pharmaceuticals, and textiles due to limited space in urban environment. There is usually a strict requirement of machine operation on the building vibration, so that it becomes relevant to quantify the structural dynamical characteristics, e.g., natural frequencies, damping ratios, and mode shapes. This paper presents a multisetup ambient vibration test and retest conducted in a multistory industrial building located in Haining, China, using eight triaxial accelerometers to cover 36 measurement locations in six setups during normal operations and shutdown condition. These tests followed a nearly identical test configuration with minor local discrepancies. To obtain the primary modal parameters of the structure, a Bayesian multisetup modal identification algorithm is adopted, followed with a detailed analysis of the results. Furthermore, the floor serviceability is investigated, primarily focusing on worker comfort. The study reveals several interesting phenomena, such as the impact of structural expansion joints and sensor synchronization on the identification results. This study serves as a demonstrative effort in examining the dynamic characteristics of multistory industrial buildings. It provides insight into evaluating the dynamic characteristics with limited sensors and abnormal identification results. [ABSTRACT FROM AUTHOR]

The significant rainfall in October 2018 in northern Iran caused devastating flooding that caused considerable economic losses and claimed at least nine lives. Following the flooding, several bridges have suffered different levels of damage. Immediately after the event, the authors conducted a field investigation to study the performance of RC bridges during the flood. This paper presents the failure modes of flood-affected bridges and provides reasons for every failure case. The results revealed that scour around foundations, pier tilting, flexural failure of piers, and superstructure unseating were the most significant failure modes observed. Pile-supported bridges performed notably better than those with shallow foundations. In addition, observations have revealed that structural integrity plays a crucial role in the general stability of bridges during flooding. Pier ductility is another significant parameter that affects the performance of bridges during flood events. Finally, fragility curves were developed based on the empirical data set provided from the post-flood status of the bridges in the affected region. The river flow velocity (Vs), maximum flow discharge (Qp), and Qp normalized to the 10-year average river discharge (Qm) were considered intensity measures of the fragility analysis. The correlation analyses revealed that despite Vs and Qp/Qm , the flow discharge was not an appropriate intensity measure for generating fragility curves. [ABSTRACT FROM AUTHOR]

Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise structural integrity and public safety. This study develops machine learning (ML) models using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories. Models were tested and trained on inspection records from over 28,786 bridges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evaluated after hyperparameter optimization, based on 10-fold cross-validation, including logistic regression, gradient boosting, AdaBoost, random forest, extra trees, K-nearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and bagging. Model effectiveness was assessed using accuracy, recall, F1-score, and area under the curve. Results indicate the optimized CatBoost classifier achieved 96.66% testing accuracy in rating deck conditions. The incorporation of hyperparameter optimization has significantly enhanced the overall predictive performance of the models, ensuring robust and reliable deterioration forecasting. The research sheds light on crucial factors such as deck age, area, and average daily traffic, contributing to a more comprehensive understanding of the factors influencing bridge deck condition ratings. These insights inform preventative maintenance planning to extend service life. This work pioneers a data-driven framework to forecast concrete deterioration, empowering officials with precise predictions to optimize infrastructure management under budget constraints. The approach provides a promising decision-support tool for sustainable infrastructure. This paper explores the use of machine learning techniques for the deterioration prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustainability of bridge infrastructure by providing timely information and evidence-based decision making for bridge maintenance and management. Such prediction models have several practical applications such as (1) predicting when maintenance or repairs are likely to be needed; (2) assessing the risk of failure or deterioration of different components of a bridge; (3) effectively managing the bridge life cycle by providing insights into the aging process and helping authorities plan for rehabilitation or replacement strategies; (4) enabling ongoing monitoring of the performance of a bridge under various conditions such as heavy traffic loads, environmental factors, and seismic events; and (5) assisting in effective asset management by allowing for the prioritization of investments, the efficient allocation of budgets, and the planning for the long-term sustainability of the bridge infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, sensitivity of small-amplitude stiffness of a full-scale, three-story reinforced concrete flat-plate structure is investigated to assess development of damage during cyclic lateral load with up to 3.0% lateral drift ratio. The small-amplitude displacement tests were made at a maximum 0.03% drift ratio between the cycles of lateral loads causing mean roof drifts of 1.5% and 3.0% drift ratios. Results of the small-amplitude experimental program suggest that the building behaves as a nonlinear-elastic system at small displacement (i.e., drift ratio below 0.03%), and the small-amplitude stiffness compared at identical displacements can indicate damage from previous large-displacement cycles. It is shown that there is a robust and consistent relationship between stiffness and varying natural frequency measured at small displacements. As demonstrated, small-amplitude vibration properties may be an effective indicator of damage as long as a nonlinear-elastic model is used instead of a traditional linear model. [ABSTRACT FROM AUTHOR]

Facility maintenance decisions are essential to ensuring infrastructure can continue to meet mission objectives. To keep facilities and equipment operational during budget shortfalls, sometimes myopic maintenance strategies are used due to their immediate benefits despite their longer-term suboptimal effects on facility health. This effect is even more pronounced when making maintenance plans across multiyear horizons. Often, these multiyear effects require nonlinear modeling to capture their complex interactions. This paper explains how to frame such a scenario as a linear programming problem by utilizing a degradation model and a predefined set of maintenance actions, like repairing, replacing, or performing no maintenance on a component. Translating to a linear program has many benefits, like guaranteed optimality and shorter computation times. Case studies show how this approach improves outcomes across different scopes, ranging from a single facility to a portfolio of facilities. The results show significantly better utilization of resources in terms of the resulting Building Condition Index for the same budget. [ABSTRACT FROM AUTHOR]

Masonry walls are commonly preferred in the construction of low-rise buildings due to their proven efficiency in resisting gravity loads and their relatively low material and labor costs. However, few studies investigating the behavior of retrofitted buildings under blast loading have been found in the literature. This paper describes the first comprehensive study investigating the efficiency of four retrofitting techniques for a low-rise masonry building under blast loading. The investigated retrofitting materials were textile-reinforced mortar (TRM), carbon-fiber-reinforced polymers (CFRPs), glass-fiber-reinforced polymers (GFRPs), and polypropylene bands (PPBs). Compared with regular reinforcing steel, these materials have light weight, high corrosion resistance, and perfect durability properties, in addition to their ease of application. A three-dimensional applied element model (AEM), which accounts for both geometric and material nonlinearities, was developed and validated to analyze an existing four-story masonry building under blast loading. Material nonlinearity was considered by including nonlinear models for masonry, mortar, TRM, CFRPs, and PPBs. Masonry blocks were modeled using brick elements, while the in-between mortar was modeled using spring elements. Validation of the AEM was by modeling masonry walls taken from the literature and comparing their results with counterparts obtained from experiments from the literature. Cracking pattern, displacements, support rotation, and load capacity for the investigated building with and without retrofitting using the four materials were compared. Costs were compared based on market prices. Retrofitting the case study building with TRM, CFRPs, GFRPs, and PPBs reduced maximum support rotation by 39%–98%, 28%–88%, 33%–92%, and 9%–84%, respectively, and increased blast load capacity by 35%–404%, 48%–91%, 83%–135%, and 26%–46%, respectively. The study revealed that retrofitting the investigated building with TRM provided the optimum behavior in terms of cost, crack propagation, and blast load capacity. Masonry walls are frequently used in low-rise buildings due to their efficiency in resisting gravity loads and having low material and labor costs. This study investigated the efficiency of textile-reinforced mortar (TRM), carbon fiber–reinforced polymers (CFRPs), glass fiber–reinforced polymers (GFRPs), and polypropylene bands (PPBs) to improve the resistance of low-rise buildings to blast loading. The adopted retrofitting materials have light weight, high corrosion resistance, and perfect durability properties. A numerical model analyzed an existing four-story masonry building under blast loading. Cracking, displacements, support rotation, and load capacity with and without retrofitting were compared. Costs were compared based on market prices. Retrofitting the studied building with TRM, CFRPs, GFRPs, and PPBs reduced maximum support rotation by 39%–98%, 28%–88%, 33%–92%, and 9%–84%, respectively, and increased blast load capacity by 35%–404%, 48%–91%, 83%–135%, and 26%–46%, respectively. The study findings recommend TRM in retrofitting masonry buildings to achieve the least cost and the fewest cracks in addition to the highest blast load resistance. [ABSTRACT FROM AUTHOR]

Thermally induced stresses can build up in rigid pavements without pressure relief joints. The pressure created becomes a problem for adjacent structures such as bridges. The pressure can cause the pavement to push on the bridge and close the bridge expansion joints. This sets the stage for additional distress if not repaired quickly. This paper presents a case study where excessive pavement pressure damaged a two-span conventional bridge spanning Interstate 35 in Moore, Oklahoma. The bridge was instrumented with vibrating wire sensors to gather information on the behavior of the bridge before and after the repairs. There is a dearth of sensor verified observations of excessive pavement pressure acting on conventional bridges. The sensor data provided in-depth information on the response of the distressed bridge before and after repairs to thermal loading. Prior to the repairs the approach pavement was pushing on the bridge deck and causing the abutment backwalls to tilt toward the bridge. Following the repairs, the approach pavement is no longer pushing on the bridge deck. However, the abutment backwall is now moving in response to thermal changes in the bridge deck, likely due to excessive friction between the deck and the abutment backwall. U-shaped shaped cracking and spalling, previously observed in integral abutment bridges, was observed at this conventional abutment bridge, and the mechanisms causing these cracks and spalling are discussed. Through this study it was found that placement of pavement pressure relief joints relative to the bridge is crucial for reducing the magnitude of stress acting on the bridge. It was also found that the pavement stress acting on a bridge deck can be estimated with vibrating wire strain gauges given a baseline strain response for the approach pavement. [ABSTRACT FROM AUTHOR]

Fire accidents often pose severe threats to the safety of steel bridges. This paper presents the numerical study of the Runyang Suspension Bridge (RSB) fire accident with an emphasis on its in-fire thermal effect on the hangers. This fire accident was caused by the collision of two heavy-goods vehicles (HGVs) on March 4, 2017, and lasted for 1.5 h. Based on the simulated temperature, the structural safety of four burned hangers during and after the fire accident was evaluated and compared. In general, the simulation results are consistent with the actual situation, and the evidence for the replacement of the hangers is sufficient. The influence of wind parameters and cooling actions on the hanger safety is also discussed. It is found that the wind parameters affect the directions and tilts of the flame, so that the highest temperature and the corresponding height are influenced accordingly. Also, the efficiency of the water-cooling method is influenced by the intervention time of fire engines. If water spraying is conducted within 30 min from fire initiation, the safety of the hangers during and after the fire could be improved, as compared with air cooling. [ABSTRACT FROM AUTHOR]

This correction notice addresses an error in the article "Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better" by Wanting "Lisa" Wang and others. The correction reinstates a reference to a study by Rosenheim et al. (2021) that was inadvertently removed. The correction also provides missing information for another reference by Rosenheim (2021). The American Society of Civil Engineers (ASCE) regrets the error and has made the necessary corrections in this erratum. [Extracted from the article]

This summary discusses the article "Erosion Mitigation of Bridge Abutment Backfill during Flood Events" by Kewei Gao et al. The authors conducted laboratory experiments to examine the erosion of bridge abutment backfill materials during flood events. They focused on the backfill used by the Pennsylvania Department of Transportation (PennDOT), but the results are applicable to other highway jurisdictions. The research found that the use of geotextiles, coarse backfill aggregate, and riprap are critical for erosion mitigation. The authors also discussed the characteristics of the geotextile and riprap materials used in the experiments. However, some aspects, such as the justification for the permeability of the geotextile, could have been further explained. The article provides practical recommendations for practitioners and highlights the importance of preventing erosion to ensure the safety of bridge structures. [Extracted from the article]

This document is a closure to a case study on ground subsidence caused by the drying effect of Australian native eucalypts. The study highlights the potential for clay soil shrinkage and subsequent damage to structures such as residential buildings, road pavements, and pipelines. Recommendations for addressing the drying effects of trees on nearby buildings vary by country, with the Australian standard AS 2870 providing guidelines for designing footings near trees. The closure also addresses potential queries from practitioners, including the age of the trees, the construction date of the netball court, and the impact of cutting tree roots during barrier construction. The writers express gratitude to Dr. Vishnu Diyaljee for their insights and thank the discusser for their valuable questions and additional insights. [Extracted from the article]

This document is a closure to the article titled "Erosion Mitigation of Bridge Abutment Backfill during Flood Events." The closure addresses some points raised in a discussion by Dr. Diyalgjee. It explains that the suitability of a geotextile for retaining backfill materials should consider the apparent opening size (AOS) of the geotextile in relation to the grain size of the aggregate being retained. The closure also discusses concerns over constructability and the impact of scour on the integrity of the reinforced embankment. It concludes that timely inspections and repairs are important to ensure the continued operation and minimize degradation of the geotextile. [Extracted from the article]

This study addresses the limitations of existing indirect bridge damage identification methods that are based on the vehicle–bridge coupled vibration theory of highway bridges. To overcome these shortcomings, we propose an extended approach that incorporates various types of deep-learning models with vehicle–bridge coupled vibration responses. The proposed method is demonstrated using a three-span continuous beam bridge as a case study. First, a vehicle and bridge analysis model is established, and bridge damage is simulated using unit stiffness reduction, considering different damage scenarios. Next, to account for road roughness randomness, vehicle–bridge coupling vibration analysis is performed under various road roughness conditions, yielding the vertical acceleration vibration signal of the vehicle. Subsequently, we employ an end-to-end damage recognition method, utilizing the vehicle acceleration response as the network input, to construct two types of deep-learning models: one-dimensional convolutional neural network (1D-CNN) and convolutional long short-term memory neural network (CNN-LSTM). The recognition performance of both models is compared and analyzed. Taking Zhengzhou Taohuayu Self-Anchored Suspension Bridge in China as an example, this study delves into the capability of bridge damage identification using deep learning. The results demonstrate that the one-dimensional convolutional neural network achieves excellent recognition performance in terms of both damage location and severity. [ABSTRACT FROM AUTHOR]

This study presents the structural damage classification of a large-scale bridge, considering several damage scenarios using One dimensional convolutional neural network (1D-CNN). Measurements obtained from the Z24 bridge in Switzerland during the short-term progressive damage tests are used for this study. Acceleration responses at 291 sensor locations are measured under forced and ambient excitations. This study considers only the measurements under ambient excitations, which has the advantage over forced excitation of not required to measure the excitations. Furthermore, to reduce the overall cost of monitoring the structure, this study aims to use fewer sensor measurements. Out of 291 sensors, only three measurements are used in this study. Each measurement contains 65,536 samples collected at a sampling frequency of 100 Hz. The measurements from three sensors are processed into shorter lengths of 150 data points, each with a 50% overlap. The processed data are inputted to the proposed 1D-CNN model. The proposed 1D-CNN consists of two 1D-CNN networks with different kernel sizes to perform better with different abstract features. The flattened outputs from these two networks using the same input are concatenated and fed into a fully connected dense network for damage classification. The labelled outputs are the different damage scenarios introduced in the progressive damage tests. The performance of the proposed approach is measured in terms of accuracy supported by a confusion matrix. The performance is measured for three cases. The result indicates that better performance is obtained compared to a previous study with the fused features as input to the deep learning models, although fewer sensors are used. Practical Applications: The findings from this study demonstrated that a good damage classification could be achieved using fewer sensor measurements from a large-scale bridge. The Z24 bridge benchmark data are used as an example in this study. Several damage scenarios were considered during the progressive damage test, and all tests were performed under ambient and forced excitation conditions. A multi-headed, one-dimensional convolutional neural network is proposed to classify the damages using the ambient condition data set. The performance is compared with an existing study using the same data set, but the data pre-processing techniques and model are improved. Three cases are defined by varying the size and length of the available time-series data. The proposed model has obtained better damage classification results for all the cases than an existing study. The advantage of the study is that the damage classification is performed using data obtained from a real large-scale structure under ambient conditions, eliminating the need for external force excitation. The proposed method could also be used for condition monitoring and safety evaluation of aerospace and mechanical structures. [ABSTRACT FROM AUTHOR]

Given that PVC membranes are a current solution for waterproofing flat roofs, and their use has been increasing in Portugal, it is important to analyze the main reasons for the occurrence of unusual anomalies in membranes mechanically fastened to the roof (not protected, directly exposed to the normal atmospheric agents) such as the premature occurrence (after less than 8 years old) of very localized dark spots on its surface that are longitudinally along all the rolls of the membrane. An experimental study was carried out and essentially it showed a clear decrease in the thickness of the top layer of the membrane and its total mass in these areas—already with the presence of the knots of the reinforcement mesh on the surface of the membrane, seen through an electron microscope—which is associated with very low levels of plasticizer for the service life of this membrane. From the analysis done, it can be concluded that the defect in question is associated with a less careful formulation of the PVC compound (or mix) during the manufacturing process for unprotected membranes on the roof. [ABSTRACT FROM AUTHOR]

Double-story high-speed railway stations have become increasingly popular due to their smaller footprint and improved operational efficiency. However, ensuring passenger comfort in such stations requires careful consideration of train-induced vibrations. This study investigates the train-induced vibrations in Beijing Fengtai Railway Station, which is Asia's largest and China's first double-story high-speed railway station. To carry out this work, a vibration source load model and the finite-element model of the station were first established, where the model was verified using field-measured data. Following these, the vibration characteristics of the station under various train passing conditions, including different track positions and train speeds, were investigated numerically in both time and frequency domains. The findings of this study provide valuable insights for the vibration control of the railway stations of the similar modes and scales. [ABSTRACT FROM AUTHOR]

Staged construction is often used for bridges, in which a portion of a bridge remains open to traffic while another portion is closed for construction. Although staged construction reduces delays and the need for detours, subjecting the partially constructed bridge to dead and vehicular live loading can lead to damage and subsequent deterioration, including compromised longitudinal construction joints and transverse deck cracking. Despite past studies and best practices, transverse cracking persists and is often observed more on one side of the deck during construction, and no definitive explanation has been provided. As such, the objective of this study is to present a mechanics-based method for assessing transverse deck cracking in bridges during staged construction. Demonstrated on two bridges that underwent staged construction, the assessment method provides insights into the persistence of the issue and provides a mechanics-based explanation for observations of transverse cracking sometimes being observed only on one side of the bridge. The method, based on the superposition of strains, considers the sequence of steps in the construction process and estimates strain demands in the concrete decks at each stage, considering variations of the structural configuration, applied loads, and time-dependent behaviors. The results of the assessment agree with finite-element analysis results while requiring less model development and computational effort. The estimated strain history is then used to assess whether transverse cracking occurs by comparing the demand to the concrete's tensile strength in time. The method is demonstrated on a new bridge constructed in stages and an existing bridge that underwent rehabilitation via staged construction. The results of the demonstrations suggest the importance of evaluating both the open and closed portions of bridge decks at each stage, as each portion has different structural and mechanical properties and loading demands. The assessment results are qualitatively validated based upon information from postconstruction bridge inspections. Furthermore, the method can be used to gain insight into mitigating transverse deck cracking and support decision-making for bridge construction practices. [ABSTRACT FROM AUTHOR]

Pavement markings serve to convey information to drivers, regulate driving behavior, and effectively mitigate traffic congestion and reduce accidents. Nonetheless, due to traffic exposure and temperature stress, pavement markings may develop defects to diverse degrees. Consequently, the inspection and maintenance of pavement markings has been paid high attention. Traditional manual detection methods prove time-consuming, subjective, and present security risks. Therefore, we employed four object detection models [You Only Look Once version 5 (YOLOv5), YOLOv7, faster region convolutional neural networks (Faster R-CNN) with visual geometry group laboratory (VGG), and Faster R-CNN with residual network (ResNet)] to achieve intelligent recognition of pavement marking defects through deep learning. Each model underwent 1,000 epochs of training and utilized 2,000 annotated road inspection images. Through data augmentation, module optimization, and anchor redesign, these models can locate pavement markings and classify their defects. The accuracy and efficiency of the model were evaluated by mean average precision (mAP) and frames per second. In addition, we introduced evaluation indicators that focused on defect types to assist in selecting models with high applicability in detecting markings. Among these models, the optimized Faster R-CNN with VGG as the backbone network has an mAP of 93.96% and can detect over 28 images per second, which meets the engineering requirements. Pavement markings play a crucial role in guiding driver behavior, and they significantly contribute to alleviating traffic congestion and reducing traffic accidents. As the demand for autonomous driving technology increases, the maintenance of pavement markings has become more important. To address this, it is essential to determine which pavement markings require maintenance through inspection. However, traditional manual inspection methods suffer from issues such as low efficiency, high cost, and safety hazards. In this work, we developed four models utilizing artificial intelligence to automatically detect pavement markings in road inspection vehicle images. This approach significantly reduces the need for manual operation and ensures efficient and safe detection of pavement markings. The automatic detection results accurately match the actual position of the pavement markings, thus providing valuable guidance for maintenance work. [ABSTRACT FROM AUTHOR]

Pier columns, as primary bridge supports, are vulnerable to extreme loading demands resulting from fire, vehicle collisions, and blasts. The combined effects of these demands may cause significant strength reduction, extensive damage, and potentially collapse. Although the response of bridge columns subjected to independent or combined vehicle impact and air blast has been widely investigated, the cumulative effects of fire in conjunction with these dynamic loads have received limited attention. Consequently, this study presents a unique and advanced multistep finite-element modeling approach using LS-DYNA to simulate the response of isolated round reinforced concrete bridge columns subjected to fire prior to and after a vehicle collision and subsequent air blast. The developed modeling approach incorporates uncoupled implicit heat transfer analyses and explicit structural analyses and was validated against published fire, impact, and blast test results. A parametric study that investigated the effects of various column diameters and fire exposure scenarios on performance was completed, considering fire occurring before or after impact and blast. Column performance was assessed based on damage levels, residual load capacities, and lateral displacements. Study findings enhance the understanding of bridge column performance under this combination of hazards, provide insight into improving bridge resiliency, and could potentially have practical implications for the design and maintenance of bridge columns. Specifically, the study reveals that fire prior to impact and blast was a more critical load sequence for larger column diameters that could potentially remain in service in their final damage states while being repaired. Moreover, the study indicated that for the same fire duration and column diameters, residual capacities and displacements corresponding to half-surface-area exposure were slightly lower than those exposed to fire on the full surface area, a finding that could be beneficial in many practical applications where protective techniques and tools could be implemented. [ABSTRACT FROM AUTHOR]

Hybrid columns with partial structural steel-reinforced concrete (SRC) and reinforced concrete (RC) sections in the transfer stories of buildings are prone to premature shear failure due to sudden changes in load bearing and stiffness properties. An experimental program was undertaken in this study to investigate the influence of the length of embedment of structural steel in hybrid SRC-RC columns bending around strong and weak axes. Five large-scale specimens with varying lengths of embedded structural steel were tested under gradually increasing lateral cyclic displacements and constant axial load. The main parameters studied were the failure mechanism, hysteretic response, energy dissipation potential, and lateral stiffness of test specimens. Test results showed that all SRC-RC specimens, irrespective of the direction of lateral loading, had higher lateral strengths than the RC columns. However, specimens with the lower embedment of structural steel performed poorly due to shear-dominated failure. Test results also highlighted the detailing requirements for lateral ties in the zone where structural steel was not present to promote the flexural mode of failure. Numerical analysis based on finite element modeling was carried out to predict the strength and failure mechanism of SRC-RC columns. Based on the findings of this study, recommendations are suggested for the design and detailing of these columns for practical applications. [ABSTRACT FROM AUTHOR]

Recent collapses of reinforced and prestressed concrete bridges have been attributed to corrosion and construction flaws, which are often belied by an outward appearance of robustness. The true nature of degradation being obscured, routine monitoring may fail to capture serious defects. Two simple analytical methods are presented herein for the prediction of corrosion damage evolution as a means of supplementing routine inspections. The first approach captures load redistribution between concrete and corroded reinforcement, based on the principles of compatibility and equilibrium. Corrosion is modeled by decreasing the effective reinforcement area and reducing the reinforcement strength and ductility according to models from the literature. The second method couples corrosion and fatigue damage by treating corrosion pits as fatigue initiation sites. It is shown that corrosion pits reach a critical point when their depth approaches one-half of the reinforcement diameter. Both schemes are implemented in a case study of the Morandi Bridge, inspections of which revealed the presence of internal corrosion damage and construction flaws upon its partial collapse in 2018. [ABSTRACT FROM AUTHOR]

This article discusses a case study on the effects of ground subsidence caused by the drying effect of a group of Australian native Eucalyptus trees near a netball court. The study demonstrates the impact of evapotranspiration on the cracking of the court and highlights the importance of monitoring sap flow from trees to understand water movement. The article also compares the recommendations for tree placement near infrastructure in Canada and Australia, emphasizing the need for geotechnical engineers to consider the influence of trees on foundation distress. The study suggests the use of root barriers and collaboration with agriculturists to mitigate tree-induced suction and prevent settlement issues. The article concludes by calling for a reevaluation of the Canadian Foundation Engineering Manual to address the influence of trees on foundation behavior. [Extracted from the article]

This correction notice addresses errors and omissions in the article "Early Detection of Honeycombs in Concrete Pavement Using GPR" by Theresa McCabe, Ece Erdogmus, Antony Kodsy, and George Morcous. The correction includes the correct dimensions of the slabs tested, the correct embedded depths of the air voids, the correct identification of the ground-coupled antenna, the replacement of a word in a sentence, the correction of the vertical axis in a figure, the correction of the units in another figure, and a correction in the acknowledgements section. These corrections have been made to preserve the accuracy of the published version of the article. [Extracted from the article]