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The preservation of the degrading transport infrastructure is vital, as replacing it is impossible in view of limited budgets and environmental impacts. One component in overcoming this challenge is the evaluation of the reliability and the calculation of the remaining service life of existing structures. These structural reliability reassessments allow for the identification and utilization of load‐bearing capacity reserves. The consideration of actual structural characteristics and environmental conditions, various of which can be gathered by means of numerous measurement and inspection techniques, has the potential for more realistic computation results and therefore more economic decisions about the operation as well as maintenance activities. This article attempts to shed light on the potential of nondestructive testing (NDT) methods for updating the input variables in the ultimate limit states during a recalculation. The approach of the NDT‐supported reliability reassessment is demonstrated using a prestressed concrete bridge emphasizing the bending proof in transversal direction. As a result, the semi‐probabilistic, probabilistic and NDT‐based reassessment results are compared and the effects of NDT‐supported structural analysis on the calculated reliability are highlighted. [ABSTRACT FROM AUTHOR]

Fire is a sporadic disaster of concrete bridges, with diverse fire environments and complex damage mechanisms. Accurately evaluating the damage situation of concrete bridges after a fire is exceedingly challenging. This study formulates a damage analysis and assessment method based on the step-by-step and progressively deepening working principle. The method relies on fire scene numerical reconstruction and encompasses key technical aspects, including bridge detection and analysis during the fire incident, fire scene numerical reconstruction, and subsequent bridge damage assessment. Building upon these principles, the study utilizes results from the detection and analysis of the concrete T-girder bridge during a fire incident to establish Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) models for the numerical reconstruction of the fire scene. These models enable the calculation of varying temperature distributions and the evolution of the bridge under fire. Compared with the parameters obtained through the ISO834 method, the numerical reconstruction approaches not only enhances the accuracy of replicating the bridge combustion process but also enables the extraction of temperature field distribution patterns within the bridge fire space and its concrete components. [ABSTRACT FROM AUTHOR]

There is a demand for research to better understand the structural performance of segmental bridge joints. In this context, the main objective of this work is to present a 3D numerical modeling using the Finite Element Method (FEM) to physically simulate the behavior of concrete joints in a segmental concrete bridge. The analysis was conducted using the design of a real bridge, and the structure studied was the main span of the New Guaíba Bridge, located in Porto Alegre, RS, Brazil. The modeling was implemented using ANSYS software, version 21.2. The simulation considers a complete model of the span to provide a more realistic determination of the joint openings and the stress distributions in the elements. It is crucial to emphasize that the results obtained from the model do not reproduce the observed behavior of the studied bridge. The analysis considered only one span with simplified boundary conditions and higher loads than those typically adopted in usual design procedures. [ABSTRACT FROM AUTHOR]

Many arch bridges have been built in near-fault regions around the world and may be affected by fling step, which is the permanent ground displacement caused by the long-period pulse within the frequency content of an earthquake. This study investigates the effect of fling step on reinforced concrete deck-type arch bridges with short to medium spans. Three-dimensional non-linear models of three existing reinforced concrete arch bridges in Iran, with span lengths of 23, 35 and 60 m, were developed. The structures were subjected to seven near-fault earthquakes in longitudinal and transverse directions, once with the fling-step pulse included and once with it removed. The results showed that fling step has a noticeable effect on the seismic response indicators investigated, but may increase or decrease the seismic demand. The maximum curvature in the presence of fling step in relation to that without fling step ranged between 0.54 and 2.06. Unseating in the longitudinal direction was most sensitive to the fling step, and showed a more than 200% increase in some cases. Effects of the fling step were correlated with the ratio of the period of the fundamental mode of vibration of the structure to that of the fling-step pulse. [ABSTRACT FROM AUTHOR]

This paper analyses the vulnerability of concrete bridges to fire through a comprehensive review and proposes strategies to overcome the fire hazard. As part of mitigation strategies, the application of advanced analysis is illustrated through developing a non-linear transient finite-element model for fire resistance evaluation of prestressed concrete bridge girders. The numerical model is validated using results from fire tests on concrete beams. The model is applied to investigate the effect of girder shape (rectangular and I-shaped sections) and strength of concrete (normal- and high-strength concretes) on the fire resistance of typical concrete bridge girders. Results from the analysis clearly show that fire resistance of high-strength concrete (HSC) girders is lower than that of normal-strength concrete (NSC) girders, which is attributed to its faster degradation of strength and stiffness with temperature. On the other hand, I-shaped girders present lower fire resistance than similar girders of rectangular cross-section due to rapid degradation of its shear capacity. This is mainly the result of slender web widths in I-shaped girders, which results in faster progression of temperatures in the web, leading to early failure under the shear limit state. This problem can be overcome by developing relevant fire mitigation strategies, such as provision of external thermal insulation on girders of critical bridges. [ABSTRACT FROM AUTHOR]

Parametric indices are empirically related to two or more response parameters with a combination coefficient and do not conform to a physical definition of damage. The combination parameter is expected to be contingent on the loading and the state of calibration other than failure. Damage is also defined as the 'deterioration of a physical quantity' and the resulting non-parametric index does not involve any empirical constant. However, the non-parametric indices saturate much before unity. This paper first aims to assess the performance of some of the existing non-parametric indices. Experimental response until failure of a circular RC bridge pier under cyclic loading is numerically simulated for this purpose. A new physical-definition based non-parametric damage index is proposed. Setting this alternate definition as the benchmark, performance of parametric Park-Ang index, some of its variants and other non-parametric indices are studied at levels other than failure through a damage progression analysis using cyclic and seismic excitations. A parametric index and its simplified form consistent with that physical-definition are next proposed with weighted-hysteretic-energy and normalized-deformation as parameters. The same calibrated model is used in subsequent assessment. Associated combination-coefficient exhibits a stable relationship with peak drift and a loading-independent functional form is proposed. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. Practical Applications: Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. [ABSTRACT FROM AUTHOR]

The identification of damage to concrete bridge surfaces is of great significance to maintaining the durability and reliability of bridges. However, it is difficult to identify small areas of damage, especially in high-resolution images. To conduct damage identification research in a targeted manner, this paper proposes a method of multidamage identification in high-resolution images based on You Only Look Once version 5 (YOLOv5). In this paper, a data set labeled with four types of damage (crack, spallation, hole, and rebar) is used for training, validation, and testing. To ensure that the network adequately learns the damage features, the high-resolution images are cropped into subimages via the autoadaptive window cropping method (AWCM) proposed in this paper. The cropping method can crop images according to the label information and protect the damage features from destruction during the cropping process. To avoid overfitting, it is necessary to balance the volume of different types of damage. After balancing, the count for each category is as follows: crack (4,980), spallation (5,225), hole (5,211) and rebar (5,020). The balanced data set can be used to train the deep learning network and construct the multidamage identification model. After identification, the subimages, and the prediction boxes of damages in them are restored to their original high-resolution images. The results show that the mean average precision (mAP) of all classes is 94.2%, and the values for cracks, spallation, holes, and rebars are 86.9%, 98.1%, 92.3%, and 99.4%, respectively, which indicates that the proposed method outperforms the other three methods (inputting original images directly, the sliding window cropping method, and the random centroid cropping method). [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three‐dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component‐wise learning combining the initial weight transfer, to overcome the difficulty particularly found in the bridge structures, where a whole structure consists of multiple and various components. The learning method was developed and verified using point cloud data acquired in an actual concrete bridge based on the point cloud completion performance of three significant deep learning models. The effectiveness and applicability of the proposed method were shown in that it improved performances in component level in applying it to the bridge point cloud completion by the multiple deep learning models, respectively. [ABSTRACT FROM AUTHOR]

Foundation scour has been a reason for several cases of river-bridge earthquake-induced failure during recent decades. However, practising engineers often do not consider its direct effect on the seismic design procedure of such structures. The cavity around a bridge foundation is a random phenomenon depending on several uncertain parameters. This study provides a probabilistic platform to investigate the effect of random scouring on the seismic performance of a particular bridge. The procedure is implemented on an existing multi-span reinforced concrete bridge. To this end, the Monte Carlo simulation technique is utilised to generate samples of the random variables of the scour model to develop the scour hazard curve. A common type of reinforced concrete multi-span bridge is considered as a model. The Latin hypercube sampling method is employed to generate random scouring scenarios in the finite-element model, including uniform and non-uniform scour. Then, fragility curves are developed utilising cloud dynamic analysis. The results reveal that the scouring pattern is one of the most crucial sources of uncertainty. In most circumstances, uniform scour scenarios are more effective than the average of non-uniform cases. However, in some specific patterns, the effect of non-uniform scouring is dominant. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel framework for assessing the impact of construction defects on bridge deck corrosion-induced deterioration. The model developed quantifies the impact of insufficient concrete cover, damage to the rebar epoxy coating, improper curing technique, and excessive water–cement ratio on the lifespan of concrete bridge decks. Regional variations, including freeze–thaw cycles, carbonation, and deicing salt usage, are also considered in the model. Two applications for estimating deterioration and decrease of condition rating are presented. Simulation results using the model developed reveal a substantial loss of life when these defects occur. This work highlights the importance of proper construction practices in ensuring long-term infrastructure integrity. Moreover, it provides a valuable tool for assessing the impact of these common construction defects on bridge deck deterioration. This tool enables bridge managers to make better-informed decisions regarding repair operations and ask contractors for fairer compensations when defective practices are observed during construction. [ABSTRACT FROM AUTHOR]

Considering the complex construction processes involved, there are significant risks during the construction of long-span cantilever casting arch bridges. In this study, a risk assessment method for the construction process of cantilever casting concrete arch bridges was developed. The compositional elements and characteristics of safety risks in the construction of cantilever casting concrete arch bridges were clarified, and a safety risk source list that includes seven major risk sources and thirty-three minor risk sources was formed. Then, a Bayesian model for the risk analysis of cantilever casting concrete arch bridge construction was established, and a method was proposed to determine the prior and posterior probabilities of the Bayesian network using triangular fuzzy numbers. This method fully utilizes the experience of experts while avoiding the subjectivity of expert opinions. A cantilever casting concrete open spandrel arch bridge (Bridge A) with a total span length of 287 m was taken as an example, and a safety risk assessment was conducted during its construction process. The calculation results show that the construction safety risk level of Bridge A was level III. This engineering application verified the feasibility of determining key node parameters of the Bayesian network using triangular fuzzy numbers. [ABSTRACT FROM AUTHOR]

The YOLO (You Only Look Once) series has recently demonstrated remarkable effectiveness in the domain of object detection. However, deploying these networks for concrete bridge defect detection presents multiple challenges, such as insufficient accuracy, missed detections, and false positives. These complications arise chiefly from the complex backgrounds and the significant variability in defect characteristics observed in bridge imagery. This study presents BD-YOLOv8s, an advanced methodology utilizing YOLOv8s for bridge defect detection. This approach augments the network's adaptability to a broad spectrum of bridge defect images through the integration of ODConv into the second convolutional layer, processing information within a four-dimensional kernel space. Furthermore, incorporating the CBAM module into the first two C2F architectures leverages spatial and channel attention mechanisms to focus on critical features, thus enhancing the accuracy of detail detection. CARAFE replaces traditional upsampling methods, improving feature map reconstruction and significantly reducing blurs and artifacts. In performance assessments, BD-YOLOv8s attained 86.2% mAP@0.5 and 56% mAP@0.5:0.95, surpassing the baseline by 5.3% and 5.7%. This signifies a considerable decrease in both false positives and missed detections, culminating in an overall improvement in accuracy. [ABSTRACT FROM AUTHOR]

Reinforced concrete bridges are a critical component of modern transportation infrastructure, but their performance is significantly affected by temperature variations. Fluctuations in environmental temperature lead to thermal expansion and contraction of materials, potentially posing challenges to the stability and safety of bridge structures. Existing studies primarily focus on the overall impact of temperature on bridge performance, providing valuable theoretical insights; however, most of these studies are based on simplified models that fail to comprehensively account for actual temperature fields and material heterogeneity. This limitation reduces the applicability of many research findings in practical scenarios. To address these gaps, this paper conducts an in-depth analysis of the thermal expansion characteristics of reinforced concrete bridges under varying temperature conditions. The study is divided into two main parts: the analysis of temperature fields in reinforced concrete bridges, and the analysis of thermal expansion characteristics under different temperature conditions, aiming to provide more accurate references for design and maintenance. [ABSTRACT FROM AUTHOR]

This paper describes the structural aspects of the Wieselburg Bridge collapse in Austria. The original design and reinforcing details for this integral concrete bridge are discussed. The collapse of the bridge is re‐constructed based on forensic documentation of the failure. The results of structural analysis carried out using material properties obtained after failure are compared to the shear capacity obtained from various models and from test results from literature. The results are used to explain the cause of failure of the integral bridge under dead load. [ABSTRACT FROM AUTHOR]

The objective of this study is to explore the seismic fragility of reinforced concrete bridges, specifically in response to the vertical components of ground motions, utilizing fragility surfaces. The examination of bridge responses involves the application of optimally selected intensity measures through three-dimensional nonlinear time-history analyses, encompassing uncertainties in both superstructure materials and soil–structure interaction effects. In this investigation, an extended Probabilistic Seismic Demand Model (e-PSDM) is employed, leveraging fragility surfaces to concurrently consider vertical and horizontal excitations. The results obtained from this approach are compared with traditional fragility curves. This study emphasizes Pile-cap displacement and drift ratio as pivotal engineering damage parameters, acknowledging their sensitivity to the influences of both soil–structure interaction effects and vertical ground motion. The fragility surfaces derived from the study reveal a correlation between increased vertical spectral accelerations and elevated probabilities of surpassing both slight damage and collapse limit states. These observations underscore the critical significance and practical utility of fragility surfaces in the context of performance-based seismic assessment and design for reinforced concrete bridges. The findings from this research contribute valuable insights into the nuanced behaviour of reinforced concrete bridges under seismic conditions, emphasizing the relevance of incorporating vertical components in fragility assessments for a more comprehensive understanding of structural vulnerability. [ABSTRACT FROM AUTHOR]

Quantification of damage in RC bridges is a key requirement for seismic vulnerability assessment. The main aim of this study is to quantify the seismic damage of RC bridges under far-field and near-fault (pulse-like) ground motions. New definitions of "Damage Index" based on cumulative energy dissipation, and distinct "Damage States" are proposed. Different damage states are established on the basis of observed experimental and analytical results. Fiber based model centered on material strain limits is adopted while quantifying the damage during non-linear dynamic analyses. The proposed damage index is compared with some existing damage indices. Comparison indicated that existing damage models either overestimating or underestimating the damage values when compared with the experimental results corresponding to the specific loading stages. Proposed damage model shows gradual progression of damage with the progress in the loading stage. Further, in order to check the performance of proposed damage index in presence of superstructure and incorporating the effect of other structural components of bridge; a case study of seismic vulnerability assessment under the far field and near-fault (pulse-like) ground motions has been carried out. It is found that proposed damage model performs quite efficiently under seismic loadings. Incremental Dynamic Analysis is carried out and fragility curves are plotted for far-field and near fault (pulse-like) ground motions. This study will be useful for health monitoring, seismic vulnerability assessment and framing retrofitting strategies for reinforced concrete bridges. [ABSTRACT FROM AUTHOR]

The current design guidelines for concrete barriers have not been updated much since the 1990s despite (1) several experimental, analytical, and computational studies that addressed both the demand and capacity sides of the design problem and (2) substantial increases in the velocity and weight of certain types of trucks specified in the crash testing guidelines that led to significantly higher impact demands than in the previous times. This state-of-the-art paper critically reviews the findings of previous studies and critiques their proposed design models. It highlights the lack of consensus regarding the failure mechanisms that should be considered in the design of concrete barriers and contrasts the results of a variety of proposed models with current design guidelines. Based on the review, recommended models for barrier design are proposed to aid in future revisions of the design specifications. Future research directions are also identified and discussed. [ABSTRACT FROM AUTHOR]

In this study, three reinforced concrete (RC) columns with and without corrosion and different reinforcement details are tested under lateral cyclic loading. One of these columns is well-confined to represent modern RC bridge piers that are designed according to the current seismic design codes, and the second column has the same detail with corrosion damage. The third column is a lightly confined corroded column to represent aging RC bridge piers that are not designed to the current seismic design codes. The experimental results showed that corrosion significantly impacts the ductility loss more than the strength loss of the tested corroded columns. In addition, although the uncorroded column was designed according to the current seismic design code, severe inelastic buckling in the vertical bars was observed during the cyclic tests. [ABSTRACT FROM AUTHOR]

To predict the future condition of a bridge, statistical models for the time-in-condition rating (TICR) can estimate the time that a bridge stays in a given condition and then predict the future condition of the bridge. However, existing research typically uses the probability density functions as the likelihood function when the TICR is estimated by Bayesian updating, in which the change in the condition rating (CR) between two consecutive inspections is assumed to occur at the later inspection, which ignores the uncertainty of the time of the condition change. This assumption will introduce an error, which is particularly significant when the two consecutive inspections are separated over a long time. In addition, a large amount of existing bridge inspection data in China has not been fully recorded; for instance, a lot of bridge inspection data only contains the CR of the bridge from the last inspection. Current research that is based on the TICR has difficulty using this incomplete data. To solve these difficulties, this paper proposes a probability-based likelihood function for the Bayesian updating of the TICR models, which could estimate the distribution of the TICR more accurately using fully recorded or single data. The accuracy of the proposed method is verified with numerical examples, and the results from different methods are discussed. Then, the effect of using complete and single data are examined. The proposed method is applied to the CR of the superstructures of reinforced concrete bridges in Beijing that uses the real inspection data, and the future deterioration risk is evaluated using the updated TICR models. [ABSTRACT FROM AUTHOR]

The coastal salt pan of Shijiazhuang-Hengshui-Cangzhou-Huanghua Port Intercity Railway project is a highly corrosive environment. In order to ensure the long-term operation safety of the bridge structure, it is urgent to carry out relevant durability research and improve the durability of the bridge structure in this project. Through detailed investigation and analysis of the erosive environmental characteristics of the bridge engineering in this region, the exposed environment of the bridge structure is evaluated, durability design of the bridge concrete structure is carried out, and the following research results are presented. In view of the corrosive environment problems faced by railway engineering, the principle of overall design scheme is proposed. The mix ratio design of bridge pier pile-cap and castin- place pile in coastal corrosive environment is conducted, and the preparation technology of high durability concrete suitable for coastal salt pan corrosive environment is formed. The structural design of durability is studied, structural design principle and physical isolation design concept are proposed, the structural design control measures are proposed to improve durability, and the structural physical isolation measures are designed to effectively improve the durability of bridge structure. In view of the erosion characteristics of the project, additional strengthened protection measures are put forward, including the silane impregnation protection and the application of corrosion resistant reinforcement. The permeable formwork cloth is studied and applied to improve the construction quality and compaction of concrete. An exposure test station is set up in the engineering site and a wireless monitoring system for reinforced concrete durability is developed. The whole process of reinforcement corrosion monitoring is realized through the wireless monitoring system. Through the durability design and research of concrete structure of bridge in the coastal environment with strong corrosion, the purpose of improving the durability of railway concrete structure under the harsh service condition of coastal salt pan has been achieved. At the same time, The solid foundation has been laid for the later evaluation of the durability of the bridge structure during the service period. [ABSTRACT FROM AUTHOR]

This paper presents a streamlined and simplified framework to select the optimal corrosion-resistant reinforcement type and subsequently calculate the corresponding life-cycle costs for concrete bridge decks. Additionally, a design-oriented methodology that accounts for the effect of high-strength reinforcement, most notably reduced ductility and its implications on structural safety decision-making, is also included as part of the framework. The researchers developed a design-friendly high-strength reinforcement factor that is predicated on the net tensile strain of the high-strength reinforcement relative to the corresponding value of an equivalently designed bridge deck using conventional reinforcement. The framework provides two approaches for estimating life-cycle costs; the first adopts a theoretical present-value calculation from previous researchers, and the second serves as a simplified approach that directly superimposes material and repair costs over the intended service life. Example life-cycle cost estimates are then shown for a series of corrosion-resistant bar types including epoxy-coated; galvanized; stainless steel; and high-strength low-carbon, chromium (A1035) bars. A case study focusing on the ductility of high-strength reinforcement then provides further recommendations for implementing such bars into the larger framework. Lastly, two example bridge deck scenarios are highlighted to demonstrate the implementation of the proposed framework. The framework is designed to facilitate straightforward optimal design solutions for department of transportation (DOT) officials, consulting engineers, and others, for bridge deck applications where enhanced corrosion resistance is necessitated and access to more theoretical life-cycle cost methodologies is limited. [ABSTRACT FROM AUTHOR]

To enhance the mechanical properties of concrete and inhibit the development of cracks, this study designed and prepared four kinds of shape memory alloy fiber–reinforced concrete (SMAFRC) with different volume contents (0%, 0.3%, 0.6%, and 0.9%), and carried out cube compression, splitting tensile, and bending tests at two temperatures (20°C and 100°C). The SMAFRC failure samples were analyzed by scanning electron microscopy (SEM) and the failure mechanism was explored. The results showed that the SMAFRC specimen with 0.9% SMA fiber content (S-0.9) had the best mechanical properties at 20°C, and the cube compressive strength, splitting tensile strength, and bending strength were increased by 18.22%, 32.34%, and 33.20%, respectively, compared with the pure specimen (S-0). At 100°C, the strength of concrete decreased compared with that at 20°C and the SMAFRC sample with an SMA fiber content of 0.6% (S-0.6) decreased slightly at 100°C, indicating stable mechanical properties. SEM shows that the presence of SMA fibers not only acts as a bridge in concrete, but also blocks the generation and development of microcracks. [ABSTRACT FROM AUTHOR]

For the last 4 decades, concrete bridges have benefited from larger diameter prestressing strands, which have increased from 13 to 15 mm in diameter and are now the standard in the US. The largest commercially available strands in the US are 7-wire–18-mm-diameter strands, but they are not used as often despite researchers finding that they can provide longer spans and lighter structures. This research seeks to experimentally benchmark the bond of 28.6-mm-diameter grade 1780 prestressing strands to normal-weight concrete and mortar for the first time. The large-block pullout test (LBPT), the North American strand producer test (NASP), and concentrically reinforced prism (CRP) pullout test were conducted to quantify the pullout force of these strands when they were embedded in concrete as well as to determine an experimental reference value. The results showed that the strands exhibit adequate bonding to the concrete when implementing the LBPT, NASP, and CRP tests to evaluate them in combination with extrapolated values from the literature using a ratio of strand diameters. Furthermore, CRP tests indicated that the transfer length would likely be approximately 1,200 mm and the development length between 1,800 and 2,400 mm for 58-MPa concrete. It was also determined that due to their much larger surface area, the bond stress of these large-diameter strands was lower than that of 13-, 15-, and 18-mm-diameter strands for similar case scenarios of bond evaluation or transfer length estimation using prisms. However, there is limited information for comparison, and the testing program presented is the only one of its kind for this strand size. [ABSTRACT FROM AUTHOR]

During the cantilever casting process of reinforced concrete arch bridges with cantilever cast‐in‐situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200 m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502–3.608. The indexes of buckling cable stress reliability are 3.806–6.784. The indexes of arch ring stress reliability are 4.379–7.562, and the indexes of buckling tower deflection reliability are 3.608–8.123. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of ultra-high performance concrete (UHPC) collars in strengthening reinforced concrete (RC) bridge piers against heavy tractor-trailer collisions through finite element (FE) analysis. First, validated FE models of UHPC and a heavy tractor-trailer were provided. Then, FE analyses were conducted to evaluate the strengthening performance of the UHPC collar. The effectiveness of UHPC collar was compared with conventional RC collar, and the effects of varying UHPC collar thickness, height, and collar reinforcement were investigated. The results showed that the most severe damage observed on bridge piers due to heavy vehicle collisions primarily occurred below a height of approximately 2000 mm, manifested as diagonal shear cracks and plastic hinges. Therefore, the recommended minimum collar height is 2000 mm. The comparison between UHPC collar and RC collar strengthening demonstrated the superior effectiveness of UHPC collars. A 130-mm UHPC collar exhibited a similar strengthening effect as an 180-mm RC collar. Among the three investigated parameters of UHPC collar thickness, height, and collar reinforcement, the study found that collar thickness had the most significant influence on the effectiveness of the UHPC collar in terms of damage pattern, energy absorption, and maximum deflection. While collar height primarily influenced deflection, a larger collar height was beneficial in reducing pier deflection at the end of the strengthened segment. Adding a small amount of collar reinforcement improved the performance of piers; however, this improvement was limited. The findings of this study address the lack of research on using UHPC for strengthening full-scale bridge piers against heavy tractor-trailer collisions and provide valuable references for future designs with similar applications. [ABSTRACT FROM AUTHOR]

During the construction of concrete bridges, vehicle traffic capacity frequently experiences significant disruptions. To assess this impact, this article conducts a new comprehensive evaluation method. Initially, we calculate the equivalent speed and density of traffic on the construction section, subsequently analyze the functional relationship between these two parameters. By categorizing traffic into three distinct flow states, we compute the traffic congestion index. Furthermore, we establish a multi-lane traffic capacity model to estimate the vehicle traffic capacity during concrete bridge construction. This estimated capacity is then compared with pre-construction traffic capacity data. Experimental results reveal that the traffic capacity reduction rate and traffic flow change rate calculated using new methodology align well with actual values, thereby validating the high accuracy of new evaluation approach in assessing the impact of construction on vehicle traffic capacity. [ABSTRACT FROM AUTHOR]

Given the advances in technology and the development of urban development and progress in people's lifestyles, the use of bridges is considered as an important and necessary way of esophageal communication. Bridges are one of the most important tools for connecting roads and different cities, which connect different areas and create connections between people. One of the most important and necessary measures in the bridge industry is related to the repair and maintenance of bridges, which cost less than building a bridge and help to create connections between people. To this end, the present study examines the effects affecting the maintenance and repair of bridges in urban contexts and for this issue an approach under conditions of intuitive fuzzy uncertainty based on analytic network process and comprise solution methods to calculate weights and option ranking offers. However, an integrated group decision making-based soft computing approach is tailored by computing the criteria importance and experts' weights to solve the urban concrete bridge maintenance model selection problem. Finally, to validate the proposed method, a case study is used along with a comparison of the proposed method and existing methods in the literature. The final results indicate the strength and efficiency of the proposed method in calculating weights and ranking options for the project of maintenance and repair of urban bridges in Rasht. [ABSTRACT FROM AUTHOR]

In April 2021 Stavå bridge, a main bridge on E6 in Norway, was abruptly closed for traffic. A structural defect had seriously compromised the bridge's structural integrity. The Norwegian Public Roads Administration (NPRA) closed it, made a temporary solution and reopened with severe traffic restrictions. The incident was alerted through what constitutes the bridge's Digital Twin processing data from Internet of Things sensors. The solution was crucial in online and offline diagnostics, the case demonstrating the value of technologies to tackle emerging dangerous situations as well as acting preventively. A critical and rapidly developing damage was detected in time to stop the development, but not in time to avoid the incident altogether. The paper puts risk in a broader perspective for an organization responsible for highway infrastructure. It positions online monitoring and Digital Twins in the context of Risk- and Condition-Based Maintenance. The situation that arose at Stavå bridge, and how it was detected, analysed, and diagnosed during virtual inspection, is described. The case demonstrates how combining physics-based methods with Machine Learning can facilitate damage detection and diagnostics. A summary of lessons learnt, both from technical and organisational perspectives, as well as plans of future work, is presented. [ABSTRACT FROM AUTHOR]

In the evaluation of existing bridges and viaducts, relying solely on a desk study is often inadequate for determining their structural reliability. Performing a proof load test provides valuable field data that offers detailed information about the structural integrity. However, the relation between the magnitude of the load and the structural reliability is not immediately clear. This study addresses the challenges associated with determining the target load and highlights the uncertainties that play a key role. A case study is presented that shows the time-dependent character of the structural reliability and the influence of an informative and a weakly informative prior distribution in a Bayesian context. It is shown how both past traffic loads and a proof load test may contribute to the proven strength of a structure. The described method provides a starting point towards a flexible approach for proof load testing in which structure-specific knowledge levels and requirements are considered. [ABSTRACT FROM AUTHOR]

As the existing bridge stock is aging, the task of assessing these bridges becomes increasingly important. One of the assessment methods for existing bridges is load testing. Improvements in the field of diagnostic load testing are related to the use of numerical models. Improvements in the field of proof load testing focus on the safety of the execution of the test as well as the required load in the test. What is still lacking is a reflection of these recent advances in the codes and guidelines used for load testing of bridges. Two approaches are proposed to address this lack. The first approach attempts to answer fundamental questions with regard to bridge load testing through research. The second approach is to coordinate efforts and facilitate collaboration and exchange of ideas internationally through the IABMAS Technical Committee on Bridge Load Testing. In conclusion, it is expected that these efforts will form the basis of improved recommendations for the assessment of concrete bridges by load testing to be included in codes and guidelines and to serve the community of engineers faced with the task of assessing ageing infrastructure. [ABSTRACT FROM AUTHOR]

The rapid and accurate postearthquake traffic capacity or functionality loss assessment for highway bridges after a strong earthquake is crucial to the decision-making for postearthquake emergency rescue and recovery, as well as seismic resilience analysis. The postearthquake traffic capacity of a simply supported highway girder bridge designed based on the capacity design philosophy is generally dominated by the loss of postearthquake vertical load-carrying capacity of the damaged bridge column, which, however, cannot be rapidly and quantitatively evaluated by previous studies. This study develops a postearthquake vertical load-carrying capacity loss model for flexure-dominated circular RC bridge columns using multiple linear regression, which is a function of column-related structural parameters and a selected damage indicator (i.e., residual drift ratio). The database of the postearthquake vertical load-carrying capacity loss for the damaged but before collapsed RC columns is generated through numerical simulations using a loading scheme consisting of nonlinear time-history analysis followed by pushdown analysis. In order to generate a sufficient database of the vertical capacity loss of RC columns, an incremental dynamic analysis (IDA) approach is adopted to produce different damage levels on the column. The significance of the loss regression model and the significance of each corresponding regression coefficient are checked by statistical tests. In addition, the generalization ability of the loss model is also tested by 10-fold cross-validation. After that, a probabilistic postearthquake vertical load-carrying capacity loss model is developed in this study. Based on this probabilistic model, a traffic capacity fragility curve conditioned on the residual drift ratio of a given column is proposed in this study for the first time to assess the remaining functionality of a given RC column. This proposed traffic capacity fragility curve is further validated by two example columns with and without considering the uncertainty, respectively. The traffic fragility curve can be quickly generated for the target circular RC bridge column and facilitate the postearthquake decision-making for the damaged but before-collapsed column, thus forwarding to the introduced rapid postearthquake assessment for simply supported highway girder bridges. [ABSTRACT FROM AUTHOR]

In this paper, closed-form expressions to calculate both the mean annual failure rate and the confidence factor are proposed. Reliability indicators are estimated assuming a normalization between capacity and demand called I DC . Simplified closed-form expressions are obtained in accordance with the probability seismic demand analysis used in SAC/FEMA. Uncertainties associated to mechanical, geometrical, and epistemic properties are taken into account, as well as uncertainties related to the occurrence of earthquakes. A comparison of both the mean annual failure rate and the confidence factor with I DC and the expressions proposed by Cornell and collaborators is performed. The numerical approach for the mean annual failure rate is obtained to verify the approximation of the closed-form solutions. Reliability indicators are obtained using six continuous reinforced concrete bridges designed to comply with three drift thresholds (0.002, 0.003 and 0.004). The bridge systems are situated in transition soil of Mexico City. Maximum differences of 4.1% and 10.6% are obtained between the proposed expression and the numerical solution for the mean annual rate of failure, estimated for two limit states: serviceability and collapse. The confidence factor presents differences of 5.2% between the present study and the original formulation. [ABSTRACT FROM AUTHOR]

Direct shear strength (DSS) of the precast concrete dry joints (PCDJs) is one of the critical issues for ensuring the structural integrity and performance of the precast segmental concrete bridges (PSCBs). However, the effects of shear key configurations, such as size, shape, spacing, and number of the PCDJs, were studied within narrow value ranges and were not well considered in the widely used AASHTO formula, resulting in unconservative predictions of the formula for multiple-keyed PCDJs. This study investigated the effect of shear key configurations within wider value ranges and incorporated a reduction factor into the AASHTO formula through a newly established parametric finite-element (FE) simulation. The simulation included an automatic procedure of model generation and calculation to derive the DSS of PCDJs based on 13 input parameters of the joint and was validated by a comprehensive database including 111 DSS results of PCDJs collected from existing literature. A parametric study was then conducted on a customized joint, which allows for exploring the PCDJs with large key sizes or numbers. Finally, a reduction factor was proposed to the AASHTO formula based on the test and simulation data considering the effects of key configurations. Results demonstrate that the proposed FE simulation is able to consider the reduction effect from the key configurations and reaches an excellent agreement in predicting the DSS of PCDJs in the database with more accurate mean values (MVs) (0.95 and 0.98), higher R2 values (0.63 and 0.69), and lower root-mean-squared error (RMSE) values (1.00 and 0.79 MPa) for both single-keyed and multiple-keyed joints compared with the results of the AASHTO formula. The parametric study revealed that the ASSHTO formula becomes increasingly unconservative with the increase of key numbers. The reduction factor needs to be included in the formula when multiple-keyed or large-size keyed joints are considered. The AASHTO formula with the reduction factor leads to a conservative prediction performance in the multiple-keyed joints, which is similar to that in the single-keyed joints. [ABSTRACT FROM AUTHOR]

Precast slab bridges are an attractive option for short- to medium-span bridges; however, the presence of longitudinal joints and concrete overlays adds great complexity to the transverse load distribution (TLD) analysis. Currently, one of the most commonly used methods for simplified analysis relies on the semiempirical formulas provided in AASHTO LRFD Bridge Design Specifications; yet, these formulas have certain limitations in their applicability. This study presents an algorithm based on the transfer matrix method (TMM) to determine the TLD of precast slab bridges. A connection model utilizing shear-and-torsion spring hinges (STSHs) is proposed to simulate the structural behavior of shear keys and the overlay of precast slab bridges in TLD calculations. The transfer matrices and transfer equations between slabs and between joints are established. The TLD for slabs is then computed by introducing boundary conditions and solving transfer equations. In addition, the finite-element method (FEM), along with the existing field tests of two different bridges, is used to verify the accuracy and validity of the proposed method. The study concludes that the TMM shows high algorithmic efficiency compared to the FEM modeling procedure and better precision and applicability than the semiempirical equations provided in AASHTO LRFD. Moreover, extended parametric studies are conducted on the effects of the thickness of concrete overlay and the relative flexibility coefficients of slabs on the TLD of bridges. [ABSTRACT FROM AUTHOR]

Prestress force dominates the carrying capacity of concrete girders, and is vital for bridge health monitoring. Many vibrational-based methods have been proposed to determine prestress using bridge responses induced by known excitations, which means that they can barely use the normal traffic of in-service bridges as excitation to achieve long-term monitoring. Moreover, most studies are based on beam theory, which may not be precise for plate-like bridges. Hence, this paper establishes a motion equation for a prestressed slab via Kirchhoff's plate theory and proposes a two-step procedure to assess the prestress and general excitation simultaneously through only bridge responses. The excitation is determined in the first step via the Load Shape function method and used as input for the prestress identification via state-space formulation in the second step. A numerical study on a prestressed plate subjected to a moving load is conducted. Considering different levels of measurement noise and load speed, the proposed method can determine the prestress and moving load with 7.33% and 10.18% error, respectively. A laboratory test on a prestressed box girder subjected to a fixed cyclic load is performed, the prestress and cyclic load are both determined to have good stability, and the errors are under 11.32%. [ABSTRACT FROM AUTHOR]

This study delved into the integration of carbon nanotubes (CNTs) in Ultra-High Performance Concrete (UHPC), exploring aspects such as mechanical properties, microstructure analysis, accelerated chloride penetration, and life service prediction. A dispersed CNT solution (0.025 to 0.075 wt%) was employed, along with a superplasticizer, to ensure high flowability in the UHPC slurry. In addition, the combination of high-strength functional artificial lightweight aggregate (ALA) and micro hollow spheres (MHS) was utilized as a replacement for fine aggregate to not only reduce the weight of the concrete but also to increase its mechanical performance. Experimental findings unveiled that an increased concentration of CNT in CNT1 (0.025%) and CNT2 (0.05%) blends led to a marginal improvement in compressive strength compared to the control mix. Conversely, the CNT3 (0.075%) mixture exhibited a reduction in compressive strength with a rising CNT content as an admixture. SEM analysis depicted that the heightened concentration of CNTs as an admixture induced the formation of nanoscale bridges within the concrete matrix. Ponding test results indicated that, for all samples, the effective chloride transport coefficient remained below the standard limitation of 1.00 × 10−12 m2/s, signifying acceptable performance in the ponding test for all samples. The life service prediction outcomes affirmed that, across various environmental scenarios, CNT1 and CNT2 mixtures consistently demonstrated superior performance compared to all other mixtures. [ABSTRACT FROM AUTHOR]

In order to study the stiffness degradation of pre-stressed concrete T-beams throughout their entire service life, full-scale destructive tests were conducted on newly constructed T-beams. The test process characterized the relationship between load and deflection at different load levels. Meanwhile, the equivalent stiffness and short-term stiffness of the T-beams at different load levels were calculated based on formulas from the code, and a segmental stiffness back-calculation method for pre-stressed concrete T-beam bridges was proposed based on system identification theory. The results show that, during the destructive test process, the T-beams experienced complete, partial linear, and non-linear stages. By using the equivalent stiffness and short-term stiffness to predict the stiffness changes during the overall failure process of the T-beams, it was found that, when cracks appeared, the stiffness of the T-beams decreased by 31% compared to the initial value, and the stiffness continued to decrease as the cracks extended further. The segmental stiffness system identification back-calculation method more accurately described the destructive test process of each section of the T-beams. This method can help to evaluate the extent of damage in each section of the beams during the overall destructive test process and further assess the overall structural integrity of the beams. [ABSTRACT FROM AUTHOR]

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of "double detection + single segmentation" based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering. [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]

A lot of bridges are aging and reaching the anticipated service life. To gain insight in their remaining resistance, inspections and measurements can be performed. The resulting information and data can be used to update variables in the degradation models of these bridges. To account for spatial variation, degradation variables can be modeled with random fields. The random fields of the degradation variables are updated based on a Bayesian inference procedure, where different types of heterogenous and indirect data are accounted for. Nevertheless, in this procedure, various assumptions need to be made, such as the quantification of measurement uncertainty, the structural and degradation model to be used, the prior distributions of the variables of interest, and so forth. These assumptions can influence the results of the Bayesian inference. Hence, in this work, it will be investigated what the influence is of different assumptions and how they affect the localization and quantification of damage. This will be done by application to a reinforced concrete girder bridge. The main conclusions are that the model used in the Bayesian inference procedure should resemble the actual structure as good as possible and that simplifications that are allowed in the design can lead to posterior distributions deviating a lot from the actual situation. Moreover, it is illustrated how information from visual observations can be included in the definition of the prior distributions and how this has a beneficial effect on the posterior predictions. [ABSTRACT FROM AUTHOR]

With the rapid development of bridge construction, the service life of bridges and traffic volume continue to increase, leading to the gradual appearance of diseases such as potholes and cracks in bridge deck pavements under repeated external loads. These issues severely impact the safety and service life of bridges. The repair of bridge deck potholes and cracks is crucial for ensuring the integrity and safety of bridge structures. Rapid repair materials designed for this purpose play a critical role in effectively and efficiently addressing these issues. In order to address the issues of pavement diseases, this study focuses on the rapid repair of epoxy concrete for bridge deck pavements and its performance is studied using experimental methods. Firstly, carbon black, rubber powder, and other materials were used to improve the elastic modulus and aging resistance of the epoxy concrete. Secondly, the addition of solid asphalt particles provided thermal sensitivity to the repair material. Finally, various properties of the rapid repair epoxy concrete for bridge deck pavements were tested through experiments including compressive strength testing, elastic modulus measurement, thermal sensitivity testing, and anti-UV aging testing. The experimental results show that adding carbon black and rubber powder reduces the elastic modulus of epoxy concrete by 25% compared to normal epoxy concrete, while increasing its aging resistance by 1.8%. The inclusion of solid asphalt particles provided thermal sensitivity to the repair material, contributing to better stress coordination between the repair material and the original pavement material under different temperature conditions. The epoxy concrete has early strength, toughness, and anti-aging properties, making it suitable for rapid repair of bridge deck pavement. [ABSTRACT FROM AUTHOR]

This article presents techniques used to observe the shear behaviour of modified Z-shaped concrete specimens, experimental results, and a method proposed to classify the shear cracking pattern of a concrete bridge based on acoustic emission (AE). Z-shaped specimens under shear were designed with steel plate reinforcement. A finite element (FE) model for this specimen was developed to validate the concrete crack propagation. AE parameters, including the peak amplitude, counts, cumulative energy, average frequency (AF) and rise angle (RA), were found to successfully correlate with the observed cracking process in the specimens. A cluster analysis based on the K-means algorithm was proposed to automatically classify the AE signal into tensile and shear clusters. An existing bridge underwent reinforcement and then testing under two test scenarios. AE signals were detected at the web of the bridge under heavy truck loads or full random traffic flows. Using the proposed crack classification showed that the signals from the bridge belonged to shear crack clusters characterized by lower AE energy. Thus, this work developed an AE method to classify cracks in concrete shear tests and extended the method to classify the shear cracks in a bridge to provide an early warning of future failure. [ABSTRACT FROM AUTHOR]

On the shear load‐bearing capacity of thin‐walled box girders with unreinforced joints in the webs As part of the LT bridge construction method (L: Longitudinal, T: Transversal), which is being developed at TU Wien, thin‐walled hollow concrete box girders are used longitudinally and are combined with deck slab elements and in‐situ concrete. Due to the construction process, unreinforced joints remain in the webs of these box girders, while the bottom and deck slabs can be continuously reinforced due to the in‐situ concrete layers. The question arose as to how this joint configuration affects the shear load‐bearing capacity of the box girders. In order to gain a fundamental understanding, two large‐format test specimens, one with joints in the webs and one without, were investigated experimentally. The shear force capacity of the girder with joints corresponded approximately to the one of the girder without joints, showing that it was possible to transfer significant shear forces despite the joints. The joint opening proved to be characteristic for the shear load‐bearing behaviour, as the stirrups adjacent to the joint act like a suspension reinforcement. A local increase in the stirrup reinforcement in this area is therefore recommended. Non‐linear finite element analyses were carried out to analyze the effects of post‐tensioning, as used in the LT method, on the load‐bearing behavior. It was shown that the load‐bearing behavior of the post‐tensioned girder with joints largely corresponds to the one without joints. [ABSTRACT FROM AUTHOR]

Accurate detection and analysis of cracks is critical for ensuring the safety and reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a promising avenue for automated crack assessment. However, processing the massive and unstructured PCD poses significant challenges in feature extraction and crack modeling. This paper proposes a novel method for bridge crack analysis by combining PCD feature extraction with a hierarchical neural network and Rodriguez rotation. The method first extracts crack features from PCD using outlier removal, denoising, and 3D coordinate conversion. A crack analysis model is then constructed by integrating multi-scale feature extraction and Rodriguez rotation into a hierarchical neural network, enabling the capture of both local and global crack patterns. Experiments on a benchmark data set demonstrate the effectiveness of the proposed approach, achieving 92.83% feature extraction accuracy, 95.73% parameter analysis accuracy, 93.51% recognition accuracy, and 0.91 F1 score. The method also shows improved efficiency compared to existing techniques. These results highlight the potential of the proposed PCD-based approach for accurate and efficient crack analysis in concrete bridges. [ABSTRACT FROM AUTHOR]

This study proposes an efficient biobjective cable force optimization strategy for concrete cable-stayed bridges aiming to maximize the safety of the structure under both the serviceability and ultimate limit state required by the design specifications. The multitude of potential load cases within limit states renders cable force optimization approaches challenged to fully satisfy code requirements, leading to encumbered complexity and computational expense. The proposed method utilized a load decoupling approach to separate the effects induced by the cable forces from other load effects to overcome this shortcoming. Furthermore, the relationship between the structural responses and cable forces was established explicitly using the influence matrix method, which aims to eliminate the finite-element method-based structural analysis in the iterations. A practical concrete cable-stayed bridge was utilized to examine the performance of the proposed method. The Pareto optimal fronts yielded by four different multiobjective optimization algorithms show a good agreement with each other, and all the computational time costs by them are less than 50 s for each run. The comparative analysis of different cable stretching plans demonstrates that optimizing the initial stretching cable forces and the final cable forces under the bridge finished state simultaneously can significantly improve the safety of cable-stayed bridges. The results also illustrate that the proposed strategy is a high-efficiency and specification-oriented cable force optimization solution for short-to-medium concrete cable-stayed bridges. [ABSTRACT FROM AUTHOR]

Space truss analogy and collapse mechanisms are the theoretical methods which are available at present to predict the collapse loads of single-cell concrete box-girder bridges. It is observed that of the two formulations, the one based on a collapse mechanism is found to be more versatile and better suited to box sections. The eccentric load acting on the top flange of a box girder may be resolved to symmetric and antisymmetric components. The antisymmetric component may again be resolved as torsion component and distortion component. However, no researcher has extended this concept to determine an equation for the estimation of collapse load. The present study proposes a theory for calculating the collapse load of single-cell concrete box-girder bridges considering pure torsion and distortion mechanisms. The box girder is treated as a folded plate structure subjected to the vertical and horizontal components of the load in the plane of each plate. It is assumed that plastic hinges are developed at the four corners of the box girder for distortion mechanism and the cross section causes pure twisting without distortion for torsion mechanism. The proposed pure torsion and distortion mechanisms are incorporated in the existing collapse mechanism method and the modified method is validated with the experimental results available in the literature. Estimation of the ultimate load carrying capacity of existing concrete box-girder bridges is discussed in this paper. Among the various simplified theoretical models available to predict the collapse load of single-cell concrete box-girder bridges, the collapse mechanisms theory yields the most accurate results according to the existing literature. To the existing, pure bending and distortion-bending mechanisms, two more mechanisms (torsion and distortion mechanisms) are proposed in the current paper to make the collapse mechanisms theory more conclusive. With the introduction of these two new mechanisms, the collapse load of existing single-cell concrete box-girder bridges subjected to unsymmetrical loading can be predicted by taking the lowest value obtained from: (i) pure bending mechanism; (ii) distortion-bending mechanism; (iii) torsion mechanism; and (iv) distortion mechanism. This will help the bridge engineers to ascertain the functioning of existing as well as new concrete box-girder bridges. [ABSTRACT FROM AUTHOR]