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Vehicle scanning methods are gaining popularity because of their ability to identify modal properties of several bridges with only one instrumentation setup, and several methods have been proposed in the last decade. In the numerical models used to develop and validate such methods, bridge damping is often overlooked, and its impact on the efficacy of vehicle scanning methods remains unknown. The present article addresses this knowledge gap by systematically investigating the effects of bridge damping on the efficacy of vehicle scanning methods in identifying the modal properties of bridges. For this, acceleration responses obtained from a numerical model of a bridge and vehicle are used. Four different scenarios are considered where vehicle damping, presence of road roughness, and traffic on the bridge are varied. Bridge damping is modeled using mass-proportional, stiffness-proportional, and Rayleigh damping models. The impacts of ignoring bridge damping or considering one of these damping models on the modal frequencies and mode shapes identified using the vehicle response are investigated by comparing the results. The outcomes of the numerical analysis show that ignoring bridge damping in vehicle scanning applications can significantly increase the efficacy of these methods. They also show that the identifiability of the bridge frequencies and bridge mode shapes from the vehicle response decreases significantly when bridge damping is considered. Further, the damping model used impacts which bridge modes can be identified because different damping models provide different modal damping ratios for each mode. The results highlight the importance of correctly simulating damping behavior of bridges, which is often ignored, to be able to correctly evaluate the efficacy of vehicle scanning methods, and they provide an important stepping stone for future studies in this field. [ABSTRACT FROM AUTHOR]

Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance. [ABSTRACT FROM AUTHOR]

To improve shear capacity, as well as reduce on-site casting and steel consumption, a novel Y-shaped perfobond rib (Y-PBL) shear connector with ultra-high-performance concrete (UHPC) grout was proposed. The shear behavior of the Y-PBL shear connector was investigated by six groups of pushout specimens. Their failure modes, load–slip curves, load–separation curves, strain analysis, and shear transfer mechanisms were discussed. Subsequently, finite-element analysis (FEA) models were established to study the effect of parameters on the shear behavior of the Y-PBL shear connector, as well as to compare the shear capacity contributions with straight-shaped PBL (S-PBL) shear connectors. Analytical models were proposed to predict the shear capacity of the Y-PBL shear connector. The results reveal that the proposed Y-PBL shear connector has superior shear capacity and stiffness. The contribution of the perforating rebar is minor compared with the end-bearing effect of UHPC. The analytical predictions agree well with the experimental and FEA results. This study can be used to guide the design and application of the Y-PBL shear connector in steel-concrete composite bridges. [ABSTRACT FROM AUTHOR]

Suspension bridges, such as stress-ribbon, are among the simplest structural bridge systems and have the lowest structural height. The flexibility of these elegant bridges poses great challenges for designers to minimize their deformability under asymmetrical operational loads. Due to the small initial sag, such load-bearing structures also cause significant tensile forces, which requires them to have large cross-sections and massive anchor foundations. This paper analyzes an innovative suspension steel bridge structure combined with a string and a cable. More attention is paid to asymmetric loading as this is more relevant for suspension structures. The new structure is studied numerically and experimentally. It is established that the string stabilizes the displacements of the bridge under asymmetric loading. The stabilization efficiency is proportional to the value of the pre-tension force of the string. The obtained results reveal the behavior of the structure and enable an evaluation of the accuracy of the numerical results, as well as the applied modeling. In addition, the experimentally obtained results allow the evaluation of more aspects of the behavior of the new bridge, which will be useful in further studies of this type of structures. [ABSTRACT FROM AUTHOR]

This study investigates traffic load as a cause of urban decay and failure on a highway in Nigeria, using Ibadan–Oyo road as a case study. This study identifies road failure as a major city nightmare that affects the well-being of urban dwellers in the study area. The Ibadan–Oyo road is a segment of the major road intersection that connects the southern part of Nigeria with the north. The methodology for this study involves a well-structured questionnaire administered to road users to elicit primary data on the factors and effects of urban decay due to traffic load. A total of 100 Questionnaires were randomly distributed among the road users (vehicular and pedestrians) of the Ibadan–Oyo road. In all, 92 questionnaires were returned. These were analyzed using the Relative Importance Index (R.I.I) and basic statistical tools, to better understand the trend of responses. The results from the study show the factors that enable traffic load and failure include the road as a major route to various parts of the country, not obeying driving laws, poor driving habits, and poor/absence of road pavements. The stress of heavy vehicles on the Ibadan–Oyo road also proves to be a major factor which leads to incessant failure of the road, even after maintenance or reconstruction has been carried out. The failure of this urban infrastructure and the poor road networks leads to many accidents, acute vehicular fuel consumption, waste of time, breakdown of vehicles and road rage. To drastically reduce the impact of infrastructural decay and failure on highways, this study suggests the following: construction of separate routes for trucks and other heavy vehicles, provision of various parks along travel routes for heavy vehicles, and use of a weighing bridge to regulate the range of axle load, replacement of flexible paving with a more rigid design for increased strength and road life span, proper rehabilitation and reconstruction of the failed segment, provision of an alternate railway system to convey heavy goods and also reduce the stress on road transport, in-depth training of vehicle drivers and traffic personnel and lastly, enlargement of the road width. [ABSTRACT FROM AUTHOR]

The intent of this article is to analyse the interconnectedness between urban transformation and eco-heritage value over time in Tianjin from a river-city perspective. The focus is on the Hai River's (海河) contribution to the mechanisms of space and power in imperial, hyper-colonial, and globalising Tianjin. After an analytical excursus of the Haihe's historical-political-economic roles, attention is given to the Haihe as the fulcrum of Tianjin's creation as a spectacle city in present times. The objectives are to elucidate the Tianjin Municipal Government-led urban 'beautification' strategy and analyse the aims and objectives of the 2002 'Comprehensive Reconstruction and Redevelopment Plan of the Haihe's Riversides' while also considering the actual experience of this transformation. The premise of this article is that the Haihe River has helped determine Tianjin's politics of design via heritagisation: the historical processes through which cultural heritage is adapted to strategically promote favourable imagery of the river-city for political management purposes. [ABSTRACT FROM AUTHOR]

Analysing the entire project life cycle is a new trend in sustainability assessment in the construction sector. Research on sustainability issues in the construction sector started with issues related to buildings, while research for other structures such as infrastructure projects and bridges was not originally an integral part of the research. Therefore, the findings, knowledge and practical applications for buildings are more comprehensive than for infrastructure projects. This paper deals with the assessment of the sustainability of bridges throughout their life cycle and emphasises the importance of life cycle analysis of bridges in the early stages of planning and design. The study provides an overview of existing life cycle sustainability assessment (LCSA) methodologies for environmental (Life Cycle Assessment LCA), economic (Life Cycle Cost LCC) and social (Social Life Cycle Assessment S-LCA) aspects. This paper provides a critical overview of the current state of the art in the field of LCSA of bridges. To identify knowledge gaps, a review of the literature on LCSA of bridges, which performs a comparative life cycle analysis of different variants or components of the load-bearing structure of bridges is provided. The main objective of the paper is to provide recommendations for conducting LCSA of bridges and to identify research directions for design criteria for sustainable bridges of the future. [ABSTRACT FROM AUTHOR]

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information. [ABSTRACT FROM AUTHOR]

Featured Application: A simple and fast tool is proposed for assessing the rehabilitation needs of bridges and the effectiveness of implementing the seismic isolation of decks to reduce the vulnerability of bridges. The creation of an isolation layer between decks and substructures has turned out to be a viable method for reducing the seismic vulnerability of existing bridges. However, to the Authors' knowledge, a practical approach for a preliminary verification of the effectiveness of this intervention is lacking. The paper introduces a practical tool for a preliminary assessment of the needs of the rehabilitation of the bridge and of the effectiveness of the deck isolation to improve its seismic performance by comparing the demands of the as-built structure and of the piers alone, expressed in terms of equivalent accelerations, to the maximum seismic acceleration allowed to maintain the substructure behavior in the elastic range. A practical implementation of the criterion is illustrated in a parametric study, considering prototypes of simply supported and continuous deck bridges with features common to the bridges of the Italian stock. The results of the study provide some indications about the inherent weaknesses of the examined pier typologies and the positive effect of the dead load of the deck on the effectiveness of deck isolation. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line's finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition. [ABSTRACT FROM AUTHOR]

The data quality determines the reliability of big data‐based bridge condition assessments. However, rapidly discerning data conditions and identifying low‐quality data segments pose considerable challenges. This study introduces a transformer‐based autoencoder neural network for rapid data quality assessment in bridge health monitoring. The average Euclidean distance was used to quantify the dispersion of multiple hidden variables, and the overall quality of the multiple data fragments was quantitatively evaluated. Moreover, a method was introduced to calculate the adaptive thresholds based on the Euclidean distance. The application to the acceleration data of a cable‐stayed bridge demonstrates that the proposed method can extract hidden variables from acceleration data segments of length 3000. The network achieves a high compression rate of 1/1500, and the extracted hidden variables retain pertinent information regarding data quality. The proposed approach is data‐driven and exhibits significant advantages in efficiency, accuracy, and user‐friendliness. [ABSTRACT FROM AUTHOR]

Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%. [ABSTRACT FROM AUTHOR]

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]

During the long-term operation of high-speed railway in our country, affected by various factors such as surrounding environment changes and human engineering activities, large lateral deviation occurs in some line bridge sections, which seriously affects the normal and safe operation of the train and needs to be rectified urgently. Based on the analysis of the principle of bridge rectification by high pressure jet grouting, the theoretical calculation model of bridge rectification is established, and the differential equations of pile deflection, rotation angle, bending moment and shear force under the high pressure jet grouting rectification mode and the analytical formula of pier top displacement are deduced based on the elastic foundation beam theory. Then,by comparing the calculated displacement at the top of the pier with the actual offset of the line,the feasibility of high pressure jet grouting technology for bridge rectification is analyzed, and the rectification effect is verified by typical engineering cases on site. Research shows that the top displacement of pier is related to foundation coefficient and excess pore water pressure generated by high pressure jet grouting; when foundation coefficient and excess pore water pressure take 1 MPa/ m and 80.3 kPa respectively,the top theoretical calculated displacement of pier is 81.8 mm,which is much larger than the actual deviation of ballastless track line of common high speed railway;In the bridge rectification project of a high speed railway Songjiang Grand Bridge,the displacement at the top of the pier calculated by the average value of the measured excess pore water pressure 30.4 kPa 6. 4~31. 0 mm,which is also greater than the maximum deviation 18.2 mm of the line, indicating that the high pressure jet grouting technology is feasible for bridge rectification theoretically;After rectification construction,the bridge and track structure are stable,the line alignment is smooth,and there is no abnormality in the normal speed operation for many years. The theory and practice have proved that the high pressure jet grouting method is a practical and feasible technical measure for bridge rectification. [ABSTRACT FROM AUTHOR]

With the construction of high-speed railway entering a new period, the frequency of railway viaduct stations has increased dramatically. When the pier is high, the space under the viaduct station has a large demand for utilization. Due to the rigidity control, the conventional pier layout is relatively dense, it is likely to form the phenomenon of "pier forest", which is not conducive to space utilization and has poor aesthetics. In order to find a reasonable structural system suitable for high pier viaduct station, this paper takes Dingyuan viaduct station of Hefei-Xinyi High-speed Railway as the engineering background, the existing problems of common structural systems are studied in the design, and a bridge structural system of sparse-piers and semi-integral viaduct station is proposed. This structural system adopts prestressed beam portal pier with cantilever with two inclined columns, which greatly reduces the number and volume of piers, and maintains good mechanical properties. A kind of fixed support which can release horizontal force is innovatively proposed, which improves the overall stiffness of the structure by changing the structural restraint system. Compared with common structural systems, it can greatly improve the utilization efficiency of the space under the bridge, greatly reduce the structure size and top displacement of piers, and has good aesthetics, practicality and structural mechanical performance. It provides a new idea for the bridge structure design of high-speed railway and other rail bridge viaduct stations. [ABSTRACT FROM AUTHOR]

Expansion joints render bridge structures highly vulnerable to damage during strong ground motions. Failures of expansion joints triggered by earthquakes not only jeopardize the post-earthquake serviceability of the bridge but also have a significant impact on the bridge's overall seismic performance. Despite extensive investigations and efforts to integrate these measures into design specifications aimed at mitigating the consequences of relative movements between adjacent bridge spans, major earthquakes have still revealed instances of damage related to expansion joints. On 6 February 2023, strong earthquake sequences occurred in Kahramanmaraş, Turkey, with magnitudes of M7.7 and M7.6. The fault lines and epicenters of these shallow earthquakes were near the city and town centers and caused severe structural damage to buildings and infrastructures. There are approximately 1000 railway and highway bridges in the earthquake-affected region. Although both highway and railway bridges have generally performed well, some bridges experienced structural damage during the Kahramanmaraş earthquakes. A large number of damage on the bridges is due to pounding and opening relative movements in expansion joints. This paper presents a comprehensive seismic evaluation of expansion joint failure mechanisms on bridges without viscous dampers during the 2023 Kahramanmaraş earthquake sequences and an in-depth investigation into the seismic performance of bridge expansion joints equipped with viscous dampers and shock transmission unit devices are implemented utilizing the strong ground motion data collected throughout the earthquake sequences. It can be stated that the near-fault induced significant directivity and fling effects, resulting in notable velocity pulses and permanent tectonic deformations, and that these effects contributed to the failures of expansion joints, viscous damper devices, pot bearings, and shear keys. [ABSTRACT FROM AUTHOR]

Road crashes, although random events, frequently occur on roads. However, certain characteristic infrastructural locations require detailed analysis regarding the frequency of road crashes. This study examines the dynamics of road crashes at characteristic infrastructural locations in Serbia from 2018 to 2022, focusing on bridges, tunnels, railroad crossings, and road work zones. Using data on road crashes from official reports, the analysis includes trends in crash rates, fatalities, injuries, and material damage during the above-mentioned time frame. In addition to the data analysis, 22 experts from the fields of traffic engineering ranked the mentioned characteristic infrastructural locations in terms of road safety. The same questions were asked to six different artificial intelligence software programs. The findings reveal significant variations in crash rates across different infrastructures, with bridges and road work zones having the highest number of crashes. Expert assessment is in line with the analysis of the results, while artificial intelligence gives a completely opposite assessment. [ABSTRACT FROM AUTHOR]

Bridges are crucial transportation infrastructures with significant socioeconomic impacts, necessitating continuous assessment to ensure safe operation. However, the vast number of bridges and the technical and financial challenges of maintaining permanent monitoring systems in every single bridge make the implementation of structural health monitoring (SHM) difficult for authorities. Unsupervised transfer learning, which reuses experimental or numerical data from well-known bridges to detect damage on other bridges with limited monitoring response data, has emerged as a promising solution. This solution can reduce SHM costs while ensuring the safety of bridges with similar characteristics. This paper investigates the limitations, challenges, and opportunities of unsupervised transfer learning via domain adaptation across datasets from various prestressed concrete bridges under distinct operational and environmental conditions. A feature-based transfer learning approach is proposed, where the joint distribution adaptation method is used for domain adaptation. As the main advantage, this study leverages the generalization of SHM for damage detection in prestressed concrete bridges with limited long-term monitoring data. [ABSTRACT FROM AUTHOR]

Accurate identification of moving vehicle loads on bridges is one of the challenging tasks in bridge structural health monitoring, but lacks of intensive investigations to merge the heterogeneous data of vision-based vehicle spatiotemporal information (VVSI) and vehicle-induced bridge responses for moving force identification (MFI) in the existing time domain methods (TDM). In this study, a novel MFI method is proposed by integrating instantaneous VVSI and an improved TDM (iTDM). At first, a novel VVSI method combining background subtraction with template matching is presented to accurately track moving vehicles on bridges. With the calibration technique and camera perspective transformation model, the distribution of vehicles (DOV) on bridges is obtained and used as a priori information in the subsequent MFI. Then, the iTDM is developed based on the MFI equation re-formed in the form of instantaneous VVSI instead of the constant speed vehicle crossing bridges assumed in the traditional TDM. Finally, based on the redundant dictionary matrix composed of Haar functions for a moving load, the MFI problem is converted to explore a solution to the atom vectors and then solved by the Tikhonov regularization method. Experimental verifications in laboratory and a comparative study with the existing three methods are conducted to assess the feasibility of the proposed method. The results show that the proposed MFI method outperforms the existing methods and can effectively identify the moving vehicle loads with a higher and acceptable accuracy. It is successful for the proposed method to replace the assumption of constant speed vehicle crossing bridge in the traditional TDM with the instantaneous VVSI in the MFI problem. [ABSTRACT FROM AUTHOR]

How to raise the bridge barrier with a concrete base height of only 51 cm to SS level of protection is not yet studied. In order to effectively retrofit an existing concrete barrier design to meet new crash testing criteria, the structural dimensions and concrete strength of the existing bridge barrier were investigated, and finite element simulation analysis was carried out, and simulation suggested the existing barrier was insufficient. Based on the structural dimension design principles of bridge barriers, the existing structure of bridge barrier was designed after adding lightweight and high-strength B750HL material crossbeams and posts on top of the concrete base, and the bearing capacity of the bridge barrier was calculated based on the yield line theory. Then, a finite element simulation analysis model was established to study and analyze the blocking, guiding, and cushioning functions of the improved design of bridge barrier. Finally, full-scale-vehicle crash tests were conducted with the SS-level small car, bus, and tractor-van trailer for this bridge barrier design scheme. This paper is the world's first to use B750HL steel as the material for the crossbeam and post of a bridge barrier with a concrete base height of only 51 cm. According to the research results, the B750HL bridge barrier, which was designed based on the calculation of structural dimension design and yield line theory, effectively reduces the increased constant load on the bridge deck caused by the extra crossbeams and posts. At the same time, it can reduce material costs and save engineering costs. After being verified by finite element simulation crash tests and full-scale-vehicle crash tests, the protective capacity of the B750HL bridge barrier was proven to meet the SS-level evaluation requirements of the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). The research findings of this paper is that the finite element simulation crash tests can effectively simulate full-scale-vehicle crash test, and the finite element simulation crash tests is reliable. If the safety performance of the barrier in the finite element simulation crash tests meets the requirements, the probability of passing the full-scale-vehicle crash test is higher. Therefore, a design scheme is proposed for the B750HL bridge barrier to improve hybrid bridge barriers at a height of 51 cm or more based on various design methods. [ABSTRACT FROM AUTHOR]

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors' knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles' vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges. [ABSTRACT FROM AUTHOR]

A novel weak-form plane frame quadrature element (WPFQE) is developed. A complex structure can be divided into several non-homogeneous (or homogeneous) elements with large size. The Chebyshev–Lobatto differential quadrature dealing with differentiation and the Gauss–Lobatto quadrature dealing with integration have the same integration points including the ends of each element. Based on the energy variational principle, the diagonal mass matrix and positive definite real symmetric stiffness matrix for the element can be obtained. The unknown quantities at the internal quadrature points of the element can be represented by those at the endpoints of the element based on the principle of static equal effect anterior to the development of global matrices, which significantly reduces the sizes of modified element matrices while maintaining sufficient accuracy, thereby obtaining the small-sized global matrices. Assembly of elements can be performed efficiently just as that in the finite element (FE) method. In the case study, the static and dynamic performances of a three-span high-pier rigid-frame bridge with variable cross-section are studied by applying the proposed method. The results indicate that the orders of structural matrices and calculation time obtained by the WPFQE method are much smaller than those of the FE method. [ABSTRACT FROM AUTHOR]

The dynamic response of a railway bridge depends on several parameters; the primary parameter is the fundamental natural frequency of vibration of the bridge itself. It is considered a critical parameter of the bridge as the driving or the forcing frequencies arising from moving trains may coincide with the fundamental frequency of the bridge and initiate a resonant response amplifying the bridge load effects. This condition may adversely affect the stresses experienced on bridge members and, consequently, the remaining fatigue life of the structure. Because the train adds additional time-varying mass to the bridge, this introduces a time-varying change in the bridge's fundamental natural frequency of vibration. As a result, train critical speeds will have a certain range depending on the train configuration. This article presents a simplified method using a power-law relationship to predict the frequency characteristics of a bridge as a function of the train-to-bridge mass ratio. The method is presented in a generalized form, which enables the frequency characteristics to be determined for any given combination of trains and simply supported bridges of short to medium span typically found on the UK rail network. The method is then demonstrated in a case study of a single-span, simply supported plate girder bridge. By considering the BS-5400 train traffic types, the proposed method is used to calculate bridge frequency effects, dynamic amplification, and train critical speed bandwidth for each train type. The simplicity of the proposed method, as it does not require any complex computational modeling, makes it an ideal and effective tool for the practicing engineer to carry out a quick and economical assessment of a bridge for any given train configuration. [ABSTRACT FROM AUTHOR]

In the multiport power electronic transformer (MPET), cascaded H-bridge (CHB) converters form the medium voltage AC port, while multiple low voltage DC ports are constructed by paralleling dual active bridge (DAB) converters into clusters. Uneven power distribution among these ports leads to power imbalances, risking over modulation in CHB converters with high power generation or load power. This study proposes a general calculation method (GCM) for determining the power boundary of MPETs, enabling the adjustment of reference power for each DC port to prevent over modulation. The GCM is designed to handle scenarios with multiple simultaneous power flow directions. The effectiveness of the GCM is verified by DC port power boundary simulation results of two independent DC bus structure multiport power electronic transformers. The proposed method provides a straightforward approach to calculating power boundary for MPETs with multiple power flow directions, ensuring efficient power management. [ABSTRACT FROM AUTHOR]

Bridge maintenance activities benefit from digital models, provided in the interoperable IFC format. Such a model, enriched with up-to-date information, is an enabler for a wide range of applications. It opens new perspectives in asset information management. However, the manual creation of a digital replica, representing the actual state of the asset from point cloud data, is time-consuming. Consequently, process automation is of particular interest. This paper proposes a systematic, semi-automatic approach for creating IFC bridge models from point clouds. It introduces new methods for semantic segmentation and 3D shape modeling. A case study demonstrates the feasibility of the process in practice. Compared to other solutions, proposed methods are robust when dealing with incomplete point clouds. [ABSTRACT FROM AUTHOR]

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks. [ABSTRACT FROM AUTHOR]

Presently, the prevailing approaches to assessing hinge joint damage predominantly rely on predefined damage indicators or updating finite element models (FEMs). However, these methods possess certain limitations. The damage indicator method requires high-quality monitoring data and demonstrates variable sensitivities of distinct indicators to damage. On the other hand, the FEM approach mandates a convoluted FEM update procedure. Hinge joint damage represents a major kind of defect in prefabricated assembled multi-girder bridges (AMGBs). Therefore, effective damage detection methods are imperative to identify the damage state of hinge joints. To this end, a stiffness-based method for the performance evaluation of hinge joints of AMGBs is proposed in this paper. The proposed method estimates hinge joint stiffness by solving the characteristic equations of the multi-beam system. In addition, this study introduces a method for determining baseline joint stiffness using design data and FEM. Subsequently, a comprehensive evaluation framework for hinge joints is formulated, coupling a finite element model with the baseline stiffness, thereby introducing a damage indicator rooted in stiffness ratios. To verify the effectiveness of the proposed method, strain and displacement correlations are analyzed using actual bridge monitoring data, and articulation joint stiffness is identified. The results underscore the capability of the proposed method to accurately pinpoint the location and extent of hinge joint damage. [ABSTRACT FROM AUTHOR]

Bridge network maintenance and rehabilitation activities are crucial to prevent bridge failure from human-made and natural hazards such as deterioration, overload, collision, fire, flooding, and earthquake. Under a limited budget, understanding the bridge closure cost to social-economic-environmental aspects can help prioritizing bridge network maintenance and rehabilitation in a cost-benefit justifiable manner. In this study, the costing method is applied to calculate social-economic-environmental costs incurred due to detour routes with additional travel time and distance. In addition to the commonly used cost factors of time value cost and vehicle running cost, this study also uses probability theory to calculate the expected consequence costs of delayed rescue services of ambulance, police, and fire-fighting caused by bridge closure. A case study with a bridge network is used to demonstrate the methodology adopted and outcome benefits from study. The results of case study show that the time value cost of vehicle passengers, vehicle running cost, and ambulance delay cost are the major cost contributors. The bridge importance ranking and consequence cost of bridge closure can be used to provide prioritized inspection and repair program for bridge components that have a high ranking under limited budget and provide benefit estimations. Practical Applications: In the real asset management of bridge networks, information on consequences of bridge closure is essential to bridge maintenance and rehabilitation (M&R) planning. For example, if the daily cost of bridge closure can be estimated particularly as a monetary value, the funding application for bridge M&R can be easily approved because it can show the M&R cost is lower than the benefit, which is to avoid the consequence cost of bridge closure. Furthermore, under a limited budget, bridges with a higher consequence cost of bridge closure in the bridge network can be prioritized for M&R planning. Understanding the consequence cost of bridge closure can also help in developing consequence-mitigated planning during actual bridge closures for planned repair. This study developed a costing methodology to estimate the daily consequence cost of bridge closure as a monetary value in terms of social-economic-environmental impacts. The results show that the time value cost of vehicle passengers and vehicle running cost are the most important contributors to the total consequence cost for bridges with available detour routes for crossing. For bridges with no alternative crossing routes, ambulance delay cost is the major contributor, followed by delay cost of police and fire-fighting services. [ABSTRACT FROM AUTHOR]

• This manuscript firstly establishes a half vehicle-pavement-continuous beam bridge (VPCB) coupled system analytical model. • It is innovatively that the bridge and vehicle initial conditions are considered into analytical model. • The pavement road surface local deterioration is simulated by increasing the local roughness level of the road surface. At present, the highway bridge field is in a period of vigorous development, and the application of continuous beam bridges is becoming more extensive, so the related safety research is particularly important. However, there are few studies on the vehicle-bridge coupled interaction of continuous beam bridges, and the influence of pavement is not considered. In order to ensure that the theoretical research can better serve the engineering practice, it is urgent to establish a more refined and more complex vehicle-bridge coupled model. In this paper, a vehicle-pavement-continuous beam bridge (VPCB) coupled system model is established. A half-vehicle model is adopted, the pavement is simulated by a continuous and uniform spring damper, and the bridge is a three-span continuous beam bridge. The motion equation of VPCB system is established and deduced using D 'Alembert principle and modal superposition method. The dynamic responses of VPCB system are calculated by Newmark- β method. Here, the continuous beam bridge mode functions are fitted based on the modal vector of the continuous beam calculated by the finite element model. The correctness of the method is verified by existing literatures. The effects of bridge pavement, vehicle speed, bridge second span length, the pavement equivalent spring stiffness coefficient k p and damping coefficient c p , road roughness on VPCB system are studied. The results show that the pavement can reduce the bridge dynamic amplification factor (DAF) and the vehicle acceleration to improve the ride comfort. The dynamic responses of the vehicle and the bridge contain each other's natural frequencies. The responses of VPCB system increase with the vehicle velocity, the vehicle responses are more affected than the bridge responses. The pavement equivalent stiffness and damping coefficient has little effect on the system. The responses of vehicle and bridge increase with the increase of the bridge second span length. When the vehicle travels each span of the three-span continuous beam bridge, the time history curve of the pavement deformation under the tire resembles a sine wave. When the local roughness level of the road surface increases, the system responses at the deteriorated position suddenly increases, and the responses of the subsequent position will also be affected. The larger the roughness grade, the more obvious the responses increase. The effect on the system responses of the whole pavement damage is more significant than the local pavement damage. [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, we present an unscented Kalman filter (UKF) for fusion of information from an accelerometer, global navigation satellite system (GNSS) instrumentation, and rotational sensor recordings of structural motion. Seismic and structural motions do not only include translations, but further incorporate torsion and twisting of the ground and/or structural components. Accelerometer and GNSS positions are known to be prone to errors introduced by rotation, such as (1) gravitational leakage, (2) misorientation, and (3) antenna pole tilt. In alleviating such effects, we propose fusion of information from six component (6C) data—3C translation and 3C rotation—and demonstrate its applicability for motion tracking on a flexible pedestrian bridge. To simulate a variety of load effects, the bridge was subjected to various sources of excitation such as hammer impulses, jumping, twisting, and running, as well as a combination thereof named the "artificial coupled forcing." The rotation errors of both the accelerometer and GNSS-estimated positions are corrected via a UKF-based fusion. We further identify the modal properties of the monitored bridge, excited by the different excitation sources, using a covariance driven stochastic subspace identification. The twisting of the bridge is shown to be a primary source of rotation errors. These errors ought to be corrected because their order of magnitude can be as large as the actual signal in the case of GNSS positions and up to 10% for accelerometer sensors. We compare the proposed UKF-based fusion for 6C motion tracking against a simplified linear Kalman filter and demonstrate the potential of the former for real-time, broadband, rotation-free displacement, velocity, and rotations tracking. [ABSTRACT FROM AUTHOR]

The bidirectional ductile end diaphragm concept uses energy-dissipating buckling restrained braces (BRB) as fuses located at the end of a bridge superstructure's floating spans. This system using BRBs can provide seismic resilient and damage-free bridges fully operational immediately after an earthquake. A shake-table testing program was conducted to subject a 1/2.5-scale specimen to series of ground motions. The specimen tested represents one span of a five-span bridge having BRBs connected to the abutment and the pier next to it. The purpose of these tests was to experimentally validate proposed connection details when subjected to the three-dimensional (3D) displacement histories (compared with the axis of the BRBs) that resulted from bidirectional ground motions and the fact that the connections must accommodate inclined BRB layouts. The test protocol included earthquake displacement histories that represent design demands, cycles of thermal excitations, and (to eventually make the BRBs fail) extreme motions. The testing program validated the effectiveness of the proposed concept and the ability of BRBs to sustain multiple ground motions before failure. [ABSTRACT FROM AUTHOR]

Objective: Numerous studies have reported that metformin can reduce the risk of tumor development. However, some of the results of these studies are conflicting, necessitating a more reliable evaluation. Methods: We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) of tumors to explore the causal relationship between metformin and tumors. Two cohorts of patients taking metformin were obtained from the UK Biobank. Complete phenotype data of the tumors were obtained from FinnGen_R10. We elucidated the causal relationship using a two-sample Mendelian randomization (MR) analysis. More importantly, we conducted a meta-analysis to ensure relatively unbiased results. In the MR analysis, we used the inverse-variance weighted (IVW) method as the main outcome indicator. Subsequently, two cohorts were integrated for the meta-analysis. Finally, we investigated the mechanisms through mediational MR analysis. Results: MR analysis revealed that metformin might have a causal relationship with 13 tumor-associated phenotypes in the training cohort. Four phenotypes were validated in the testing cohort. In the training and testing cohorts, metformin exhibited a protective effect against brain meningiomas and malignant neoplasms of the breast (HER-positive), oral cavity, tonsils, and the base of the tongue. Intriguingly, after integrating the results of the two cohorts for the meta-analysis, 12 results were statistically significant. Mediational MR analysis suggested that the effects of metformin on brain meningiomas may be weakened by the presence of the family Oxalobacteraceae. Conclusion: Metformin exhibits potential preventive and therapeutic effects on four types of tumors: brain meningioma, malignant neoplasms of the breast (HER-positive), oral cavity and tonsils, and the base of the tongue. Large randomized controlled trials are required to confirm these findings. [ABSTRACT FROM AUTHOR]

The Edmund Pettus Bridge, Selma, Alabama, a symbol of the American Civil Rights Movement and an exemplar of early 20th-century engineering, stands as a testament to the progress and challenges of its era. The bridge, recognized for its pivotal role in the 1965 "Bloody Sunday" conflict and the following Selma to Montgomery marches for voting rights, also represents significant engineering achievements with its distinctive design and construction methodology. In this study, the research team presents a comprehensive framework for documenting heritage bridges by utilizing Terrestrial Laser Scanning (TLS) technology, supplemented by other Reality Capture (RC) techniques, including Structure from Motion (SfM), 360-degree photography, and Unmanned Aerial Vehicle (UAV), and integrating the data within a Building Information Modeling (BIM) environment. The focus on the Edmund Pettus Bridge case study demonstrates how this novel approach can capture the intricate details of its structural and architectural features while preserving its historical narratives. The documentation outcomes, including a detailed BIM model and a set of Historic American Engineering Record (HAER) drawings, highlight the effectiveness of combining TLS and BIM in conserving unconventional heritage structures like bridges. This paper also discusses the technological challenges encountered, such as dealing with heavy traffic and environmental constraints during data acquisition and developing the BIM model and drawings. It outlines the strategies implemented to address these issues. This research contributes to preserving a severely under-represented American National Historic Landmark (NHL). It sets a precedent for documenting other non-building heritage structures, balancing technological advancements with historical integrity. [ABSTRACT FROM AUTHOR]

The Special Issue "4D Printing: State-of-the-art, Recent Trends, and Applications" highlights the significant impact of scientific advancements on practical and innovative applications. It focuses on the interdisciplinary challenges of multi-material printability on a voxel basis and optimizing the actuation performance of composite structures with various stimuli. Key considerations, such as mechanical strength and potential adverse effects, shape the design methods suited to specific quantitative data limitations. Four-dimensional printing calls for creativity, interdisciplinary collaboration, and practical applications. While recognizing experience-based approaches in research, this review paper emphasizes integrating science and technology through alternative strategies; innovative approaches; and the exploration of engineering, design, and artificial intelligence. [ABSTRACT FROM AUTHOR]

The seismic resilience of bridges has become an important concept in civil engineering since these systems need to remain operative during and after earthquakes. In this regard, the definition of recovery needs to consider the delay time (named as the time between an event and the beginning of the recovery process). The original concept of seismic resilience has been expanded herein in order to account for the delay time of bridge configurations. Its role in the quantification of seismic resilience has been investigated by performing a case study of a Californian highway bridge subjected to an ensemble of 100 input motions. The results demonstrate that the delay time may significantly reduce the seismic resilience of bridges. [ABSTRACT FROM AUTHOR]

Bridges are metal or concrete structures, which make road connections across rivers when installed over river paths. When flooding or scouring occurs, several issues including bridge destruction and interruption of road connections arise. There are a variety of methods of protecting such structures, one of which is application submerged vanes. This research shows how two vanes with different submergence ratio affected decrease in local scour surrounding one pier and groups of piers installed in a 180-degree bend. The scour around pier groups either in a transverse position to the flow or with a longitudinal direction was analyzed. This finding suggested that applying vanes decreased the deepest scour around the piers in all the tests, and its maximum effect occurred in triad longitudinal pier groups. By raising the submergence ratio, bed scour depth at the outer bank reached its minimum size in longitudinal pier groups. In transverse pier groups, the first scour hole near the outer bank increased in depth in the presence of vanes. By using the vanes, the slope of the scour hole towards the inner bank grew larger than that towards the other bank. The maximum slope of the outer bank happened in the triad transverse pier group at an approximate value of 0.63. The most significant impact of submerged vanes on reducing the upstream slope was observed in a single pier with 75% submerged vanes and triad longitudinal piers with 25% submerged vanes, resulting in reductions of about 50 and 74% respectively compared to piers without vanes. [ABSTRACT FROM AUTHOR]

Scour around bridges present a severe threat to the stability of railroad and highway bridges. Scour needs to be monitored to prevent the bridges from becoming damaged. This research studies the feasibility of using green laser for monitoring the scour around candidate railroad and highway bridges. The laboratory experiments that provided the basis for using green laser for underwater mapping are also discussed. The results of the laboratory and field experiments demonstrate the feasibility of using green laser for underwater infrastructure monitoring with limitations on the turbidity of water that affects the penetrability of the laser. This method can be used for scour monitoring around offshore structures in shallow water as well as corrosion monitoring of bridges.

Cascaded H-bridge inverters, which belong to strongly nonlinear time-varying system with complex dynamics, have been widely used in high-voltage and high-power engineering fields. In this paper, a carrier phase-shifted sinusoidal pulse-width modulation cascaded H-bridge inverter is taken as an example with piecewise analysis to establish the discrete iterative mathematical model. Furthermore, the nonlinear dynamic behavior of this inverter system is investigated using folding diagrams, spectrum diagrams, bifurcation diagrams, Lyapunov exponents, etc. We find that this inverter can exhibit multiperiod bifurcation, tangential bifurcation and paroxysmal chaos within certain ranges of control coefficients and other circuit parameters, which will influence the system's stability seriously. On the basis of this discovery, the sinusoidal time-delay feedback control method is proposed. The difference between the load current of the controlled object and its own delay is used to form a feedback term, which is then fed into the sine function and proportional link to obtain the control term. Moreover, the Jacobian matrix of this feedback inverter system is derived, and the constraints on the feedback control coefficient are obtained based on the Jury criterion. Simulation results have verified the effectiveness of such a sinusoidal time-delay feedback control method, which can improve the stability and anti-interference of the system. The research can be used to provide some reference and guidance for the circuit design, debugging and control of cascaded H-bridge inverters. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of obtaining a given dynamic behavior of simply supported bridge beams from the impact of rolling stock. Resonant vibrations of beams become a reality at high speeds if the train is formed of identical cars, which is typical for passenger trains. The phenomenon of suppression of beam vibrations by a train, known from publications, is possible only with an exact ratio of the length of the car and the beam. This significantly reduces the possibilities of choosing the necessary spans of beams. Consideration of issues of interaction in the "bridge–track—train" system for security purposes requires the involvement of a rather complex mathematical apparatus and appropriate software. The paper proposes a new method for determining the specified dynamic behavior of bridge beams, suitable for any spans and available to engineers at the stage of pre-design assignment of dynamic parameters of beams. The requirements that are necessary and sufficient conditions for preventing the resonance of bridge beams on the HSR are determined. At the same time, it does not require the involvement of software based on a complex mathematical technique. [ABSTRACT FROM AUTHOR]

The investigation on vehicle–bridge interaction systems (VBIS) plays an important role in evaluating the performance of bridges and/or vehicles. However, due to the limitations of traditional simulation methods and testing methods, the impact of vehicle jumping (VJ) on the VBIS has never been precisely disclosed. In order to accurately and comprehensively reveal the impact of VJ on the dynamic performance of VBIS, one real-time hybrid simulation method (RHSM) for VBIS considering the impact of VJ (VBIS-VJ) is proposed in this paper. In the proposed method, the vehicle system is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in real-time, whilst the bridge system is chosen as the numerical substructure numerically simulated in one computer. During the vehicle jumping, the vehicle and bridge vibrate freely respectively, otherwise, the experimental substructure and numerical substructure are synchronized at the coupling points in terms of force equilibrium and deformation compatibility in RHSM for VBIS-VJ. Firstly, the principle and numerical models of RHSM for VBIS-VJ are proposed. Furthermore, numerical simulations of RHSM for VBIS-VJ are conducted for verifying the feasibility of the proposed method. Finally, the influence of jumping position, jumping height, and driving speed on the VBIS is disclosed by hybrid simulations. It is shown that compared with the ever-known investigation methods, the proposed method could economically and accurately disclose the dynamic performance of VBIS-VJ. It is shown from the simulation results that compared with the referenced results, the quantitative errors of the proposed method with quarter vehicle model and half vehicle model are less than 2.5152% and 3.5599% respectively, which prove the effectiveness and accuracy of proposed RHSM for VBIS-VJ. The peak displacement of the bridge at a quarter-span and mid-span exceeds the three-quarters span by about 1.9–2.9 mm. The influence of jumping position of a quarter span and midspan of bridge is much huger than that in the three-quarters span. The dynamic response of bridge increases with the increase the jumping height, but the deformation of the bridge is reduced when the jumping height is more than 0.05 m. Meanwhile, the requirements for experimental loading equipment are acquired through the hybrid simulations, which could be used as a reference for selecting the loading system of the RHSM for VBIS-VJ. [ABSTRACT FROM AUTHOR]

In this study, a multidisciplinary approach combining a numerical analysis in three dimensions and a laboratory test is developed to investigate the dynamic coupling effect as induced by vehicle–bridge interaction in the indirect measurement. To unveil such dynamic coupling and its influence on the frequency measurement, a single degree-of-freedom test vehicle is exclusively designed with adjustable frequency, which is a low-cost, easy-to-use, and reliable tool for measuring beam frequencies in the laboratory while providing valuable data for validating numerical models, calibrating sensors, and evaluating bridge performance. From the multidisciplinary analysis, the shifts of frequencies for both vehicle and bridge are consistently demonstrated as a result of vehicle–bridge interaction effect. Moreover, for the identification of higher-order bridge frequencies, the contact-point response works better than the vehicle’s response, even with shifted frequencies being observed in the three-dimensional numerical results. As an extension, a parametric study is conducted numerically to include various practical conditions, including different types and boundary conditions of a bridge as well as pavement irregularity. [ABSTRACT FROM AUTHOR]

In geotechnical engineering, side friction resistance (SFR) is difficult to be measured directly. To further understand distribution law of the SFR, this paper developed a monitoring device that can directly measure the SFR. Further, a theoretical conversion formula for the elastic deformation and the SFR that considers the end effect of sensor sealing was proposed to guide the selection of sensor size and sealing material. Moreover, the monitoring device for the SFR was then calibrated using a largescale direct shear apparatus and analyzed the stability of the sensor. The calibration results revealed that under cyclic loading and unloading conditions, the linear correlation coefficient of the sensor was greater than 0.996, and the sensitivity after sealing could reach 4.836 ^s/kPa, which met requirements of the engineering application. The developed monitoring device characterized by simple testing principle, low cost, and high precision were successfully applied to an open caisson project in Harbin City, which contributes to address the difficult problem of efficiently collecting the SFR in highways, bridges, water conservancy, and other projects. [ABSTRACT FROM AUTHOR]

High-speed railways are “lifeline projects” that shoulder the heavy responsibility of transporting relief supplies and medical forces for the first time after earthquakes. To ensure the train’s safety after earthquakes, it is of great urgency to ascertain a post-earthquake speed threshold. To that end, a target model for seismic irregularity emerges as a key parameter. In this paper, a null phase assumption-based technique for the mutual conversion between evolutionary power spectral density and non-stationary signal was proposed. Taking a high-speed railway track-bridge system as the research object, the target model of seismic irregularities was constructed based on the proposed technique. The rationality of the target model of seismic irregularities was verified, and the construction parameter settings were discussed. Moreover, a simplified frequency-domain fitting method for the target model of seismic irregularities was proposed based on the spectral decomposition theory. According to the research findings, the null phase assumption-based technique is capable of performing interconversion between seismic irregularity and its evolutionary power spectral density with satisfactory accuracy. It is recommended to set the minimum number of seismic irregularities, spatial sampling interval, hop size, and length of window function as 50, 0.25m, 1, and 40 to 100, respectively. [ABSTRACT FROM AUTHOR]

Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis. In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation. The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making. [ABSTRACT FROM AUTHOR]

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure's temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure's natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management. [ABSTRACT FROM AUTHOR]

The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges. [ABSTRACT FROM AUTHOR]