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This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is developed, including the ultimate loading-capacity modeling of CFST members, multi-parameter selection, ultimate thresholds presetting based on the finite element method, data processing, crucial parameters determination among sub-parameters, multi-parameter regression, ultimate state prediction, and system maintenance decision-making suggestions based on the multi-parameter performance evaluation. A degenerated ultimate loading-capacity model of CFST members is adopted in the finite element analysis and multi-parameter performance assessment. The multi-source heterogeneous data processing and temperature-effect elimination are performed for the data processing. The key sub-parameters were determined by the Principal Component Analysis method and the Entropy-weight method. The polynomial mathematical model is used in the multi-parameter regression, and the ±95% confidence bounds were verified. The system maintenance decision-making model combines the relative monitoring state, the relative ultimate state by the numerical analysis, and the relative residual life of degenerated members. The optimal system maintenance decision-making suggestions for the bridge maintenance system can be identified, including the most unfavorable maintenance time and parameter index. A case study on a CFST truss-arch bridge is conducted to the proposed algorithms. The obtained results demonstrated that the crack width deserves special attention in concrete bridge maintenance. Additionally, these technologies have enormous potential for the life-cycle performance assessment of the structural health monitoring system for existing concrete bridge structures.

A succinct, real-world approach to complete bridge system design and evaluation Load and Resistance Factor Design (LRFD) and Load and Resistance Factor Rating (LRFR) are design and evaluation methods that have replaced or offered alternatives to other traditional methods as the new standards for designing and load-rating U.S. highway bridges. Bridge Design and Evaluation covers complete bridge systems (substructure and superstructure) in one succinct, manageable package. It presents real-world bridge examples demonstrating both their design and evaluation using LRFD and LRFR. Designed for a 3- to 4-credit undergraduate or graduate-level course, it presents the fundamentals of the topic without expanding needlessly into advanced or specialized topics. Important features include: * Exclusive focus on LRFD and LRFR * Hundreds of photographs and figures of real bridges to connect the theoretical with the practical * Design and evaluation examples from real bridges including actual bridge plans and drawings and design methodologies * Numerous exercise problems * Specific design for a 3- to 4-credit course at the undergraduate or graduate level * The only bridge engineering textbook to cover the important topics of bridge evaluation and rating Bridge Design and Evaluation is the most up-to-date and inclusive introduction available for students in civil engineering specializing in structural and transportation engineering.

Globally, bridges are rapidly aging, and traditional maintenance approaches face significant challenges in terms of efficiency and cost. To overcome these challenges, considerable research has been conducted to introduce enhanced bridge management systems (BMSs) based on bridge information modeling (BrIM) from various perspectives. However, most studies have highlighted the advantages of BrIM, while neglecting the practical issues that potential users may encounter on existing bridges. The primary problem is digitizing existing bridges that have not yet adopted BrIM. The universal applicability of BrIM should be carefully considered from the perspective of national maintenance authorities managing thousands of bridges, because modeling based on commercial software is expected to be time-consuming and costly. Therefore, in this study, information and functional requirements were derived from interviews with stakeholders, including bridge owners, managers, and site inspectors. Based on this understanding, a data-driven modeling approach using basic bridge information was implemented, and an inventory code system was integrated to efficiently manage and utilize the data. Moreover, mapping and deep learning-based vectorization were considered for managing inspection information, and features for bridge assessment, dashboards, and reporting were incorporated to support decision-making. The developed BrIM demonstrated the potential for enhancing maintenance efficiency through a case study. Particularly, significant improvements were observed in mandatory documentation tasks, along with their investigation and analysis, as required by regulations. Additionally, efficient modeling and data management were achieved for the existing bridge.

This study evaluates the effectiveness of an enhanced satellite-based method for estimating the construction years of Cambodian road bridges. By leveraging Landsat satellite imagery and the Normalized Difference Water Index 2 (NDWI_2), in combination with the sequential t-test analysis of regime shifts (STARS) method, this research identifies significant shifts in NDWI_2 values indicative of bridge construction activities. A total of 423 road bridges were analyzed, with the accuracy of the method assessed using the Mean Absolute Error (MAE). The improved method demonstrated considerable accuracy, with 64.3% of bridges classified as high-accuracy (MAE of 0–5), 20.8% as moderate-accuracy (MAE of 6–10), and 14.9% as low-accuracy (MAE of 11–30). Factors such as the bridge length and data quality were found to influence accuracy. A regression analysis comparing the actual bridge age to damage ratings closely aligned with the model using the estimated bridge age to damage ratings, suggesting that the the estimated bridge age obtained from the estimated construction years, in conjunction with damage ratings, provide a reliable basis for evaluating the damage trends of road bridges. The findings underscore the method’s potential for broader applications in regions with limited infrastructure data. Although effective for medium to large bridges, challenges remain for smaller bridges due to spatial resolution limitations. Recommendations for further enhancement include refining data preprocessing, segmenting bridges by size, and incorporating auxiliary datasets. This research highlights the critical role of accurate construction year estimation in effective bridge infrastructure monitoring and maintenance planning.

In Japan, bridges are inspected via close visual examinations every five years. However, these inspections are labor intensive, and a shortage of engineers and budget constraints will restrict such inspections in the future. In recent years, efforts have been made to reduce the labor required for inspections by automating various aspects of the inspection process. In particular, image processing technology, such as transformer models, has been used to automatically detect damage in images of bridges. However, there has been insufficient discussion on the practicality of applying such models to damage detection. Therefore, this study demonstrates how they may be used to detect bridge damage. In particular, delamination and rebar exposure are targeted using three different models trained with datasets containing different size images. The detection results are compared and evaluated, which shows that the detection performance of the transformer model can be improved by increasing the size of the input image. Moreover, depending on the target, it may be desirable to avoid changing the detection target. The result of the largest size of the input image shows that around 3.9% precision value or around 19.9% recall value is higher than one or the other models.

The deterioration of bridge networks poses a major threat to the availability and function of transportation systems and ultimately affects social development. Deep reinforcement learning is expected to provide intelligent decision support for bridge network maintenance. However, existing studies have neglected to explicitly consider the impact of maintenance behavior on the cost-effectiveness of bridge networks. The complex traffic environment and the interconnection of bridge networks also pose unique challenges in balancing maintenance costs and benefits. It is necessary to explore how to use the specific traffic data of each bridge in the bridge network to effectively balance cost-effectiveness and rationalize maintenance decisions. Aiming at the maintenance requirements of the bridge network, a multi-agent ranking proximal policy optimization framework is proposed. The performance of the proposed framework is rigorously evaluated using a real bridge network example. The results show that the maintenance policy based on the proposed framework can maximize the cost-effectiveness of the bridge network in its life cycle, effectively reduce the excessive risk cost and achieve a harmonious balance between different costs. In addition, the proposed framework is superior to the traditional maintenance policy and provides higher performance and efficiency.

First Published in 1999: The Bridge Engineering Handbook is a unique, comprehensive, and state-of-the-art reference work and resource book covering the major areas of bridge engineering with the theme \"bridge to the 21st century.\".

Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur.

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management.