Investigating Consistency among Bridge Inspectors Using Simulated Virtual Reality Testbeds

Unified data synthesis for automated 3D Visual Inspection and digital twinning of bridges

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R2). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R2 exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems.

Radio-Map-Based Realtime Flight Path Planning for Repeater Drones in Bridge Inspection

Optimal characteristics of inspection vehicle for drive-by bridge inspection

Bridge health diagnosis plays a vital role in ensuring structural safety and extending service life while reducing maintenance costs. Traditional structural health monitoring approaches rely on sensor-based measurements, which are costly, labor-intensive, and limited in coverage. To address these challenges, we propose a three-phase solution that integrates the Dynamic Lightweight Vision-Language Model (DL-VLM), domain adaptation, and knowledge-enhanced reasoning. First, as the core of the framework, the DL-VLM consists of three components: a visual information encoder with multi-scale feature selection, a text encoder for processing inspection-related language, and a multimodal alignment module. Second, to enhance practical applicability, we further introduce domain-specific fine-tuning on the Bridge-SHM dataset, enabling the model to acquire specialized knowledge of bridge construction, defects, and structural components. Third, a knowledge retrieval augmentation module is incorporated, leveraging external knowledge graphs and vector-based retrieval to provide contextually relevant information and improve diagnostic reasoning. Experiments on high-resolution bridge inspection datasets demonstrate that DL-VLM achieves competitive diagnostic accuracy while substantially reducing computational cost. The combination of domain-specific fine-tuning and knowledge augmentation significantly improves performance on specialized tasks, supporting efficient and practical deployment in real-world structural health monitoring scenarios.

The durability and security of the transport system plays a critical role in economic development, mobility, and the well-being of the population. Prior inspection of roads, bridges and railways has been largely laborious, time consuming and subjective. Recent developments in computer vision and deep learning (DL) open the possibility of automating the monitoring process, improving the accuracy, and facilitating the predictive maintenance. With the help of the DL models, image-based monitoring helps to detect cracks, deformations, corrosion, and structural defects with high precision. The paper provides the overall review of deep learning application in monitoring transportation infrastructure through images. The contributions made by convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and attention-based architectures are discussed in regard to their roles in automated inspection systems. Some of its applications are pavement crack detection, bridge surface inspection, railway track inspection, and tunnel inspection. As demonstrated in the review, DL performs more favourably in comparison to more traditional image processing techniques, particularly with regard to precision, extensibility, and resistance to real-world factors. The main challenges are: small labelled datasets, high computing expenses, scaling to new environments and interpreting models. However, three potentials are capable of being considered in addition to these restrictions: a hybrid approach, transfer learning, and federated learning. The paper also describes ethical, practical, and technological limitations related to the implementation of DL systems to monitor critical infrastructure. The review finds that DL-enabled image-based monitoring is a paradigm shift to smart and sustainable transportation infrastructure management. The application of dynamically executing DL systems in real time, unmanned aerial vehicles (UAVs), explainable AI, and cross-modal data fusion to enhance predictive performance are some of the research directions of the future.

This study proposes a scalable method for regional bridge health monitoring by combining Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) with unsupervised machine learning. Among various non-contact monitoring techniques, such as drone- or LiDAR-based methods, MT-InSAR provides unique advantages for large-scale bridge assessment by offering continuous, weather-independent, and cost-free deformation measurements across extensive regions. The objective of this study is to automatically detect bridges exhibiting unusual displacement behaviour using publicly available satellite imagery. A total of 48 Sentinel-1A satellite images (January 2020–December 2023) were processed using SARPROZ software to extract line-of-sight (LOS) displacements at the mid-span of 14 bridges over the Illinois River corridor in the United States. The selected bridges represent diverse superstructure types (truss, girder, arch) and transportation modes (highway, rail). MT-InSAR analysis reveals seasonal displacement trends ranging from +12 mm to −18 mm, with thermal and traffic data used to contextualise patterns. K-means clustering identifies a behavioural outlier for one of the bridges. Isolation Forest confirms this anomaly for the same bridge. The cumulative displacement of the bridge exceeds 20 mm, diverging from seasonal norms. Cross-validation using the most recent bridge inspection report reveals section loss on truss members and exposed footing at piers.

Bridge inspection using image–point cloud fusion with image filtering, damage detection and 3D registration

Corrosion Assessment Using Computer Vision, Machine Learning, and Deep Learning on Imagery Data: Evaluation and Use for Bridge Inspections

Integrating extended reality and AI-based damage segmentation for near real-time, traceable bridge inspections

The increasing use of Fiber-Reinforced Polymer (FRP) composites in bridge infrastructure presents both opportunities and challenges for structural inspection and asset management. Although FRP systems offer superior corrosion resistance and high strength-to-weight ratios, their distinct material behavior and deterioration mechanisms are not adequately addressed in existing bridge inspection standards. This study presents a comprehensive framework for the field inspection and condition assessment of in-service FRP-reinforced and FRP-strengthened concrete bridge elements, developed in a research project funded by the Federal Highway Administration (FHWA). The framework integrates findings from experimental evaluation, nondestructive testing (NDT), and literature synthesis to produce a standardized methodology compatible with the Specifications for the National Bridge Inventory (SNBI) and the AASHTO Manual for Bridge Element Inspection (MBEI). It introduces FRP-specific element identifiers, defect typologies, and condition-rating scales consistent with national bridge data structures, enabling quantitative evaluation and uniform reporting across transportation agencies. The framework represents a foundational step toward incorporating composite materials into the federally mandated bridge management systems established under 23 CFR 650.317, facilitating data-driven maintenance, lifecycle analysis, and policy development.

In response to the increasing demand for effective bridge management and the shortcomings of current proprietary solutions, this work presents an open-source, web-based platform designed to support bridge inspection and data management, particularly for small and medium-sized public administrations, which often lack personnel or funding for implementing context-specific tools. The system addresses fragmented workflows by integrating multi-format geospatial and 3D data—such as point clouds, CAD/BIM models, and georeferenced imagery—within a unified, modular architecture. The platform enables structured inventory, interactive 2D/3D visualization, defect annotation, and role-based user interaction, aligning with FAIR principles and interoperability standards. Built entirely with free and open-source tools, the P.O.N.T.I. prototype ensures scalability, transparency, and adaptability. A multi-layer navigation interface guides users through asset exploration, inspection history, and immersive 3D viewers. Fully documented and publicly available on GitHub, the system allows for deployment across varying institutional contexts. The platform’s design anticipates future developments, including integration with IoT monitoring systems, AI-driven inspection tools, and chatbot interfaces for natural language querying. By overcoming existing proprietary limitations and providing access to a versatile single space, the proposed solution supports decision-makers in the digital transition towards a more accessible, transparent and integrated infrastructure asset management.

Bridges are vital components of global infrastructure, with millions constructed over the years. Many of them face aging and are vulnerable to risks. Traditional bridge inspection methods are costly and time-consuming. They often rely on many manual laborers without providing system-level insights. Moreover, these outdated approaches make it difficult to obtain a clear representation of the current bridge health. This paper introduces a novel framework based on deep learning (DL) for identifying local bridge damage using acceleration data collected by Unmanned Aerial Vehicle (UAV)-mounted sensors. The framework employs WaveNet, which was designed as a generative audio DL model. Its causal dilated convolution deals with long-range temporal correlations without recurrence. Two WaveNet regressors are used to predict the damage location and its severity. The methodology is integrated with an optimized sensor spacing strategy for UAV deployments. The results demonstrate that the severity model achieved an average R2 = 0.98, while the location model reached R2 = 0.85. Optimal sensor spacing “S” was found at S = 1.0 m for localization and S = 0.5 m for severity. A field-simulated case was accurately identified by the two models, representing the potential of the proposed framework for more reliable bridge health monitoring.

The safety, conservation, and efficient management of existing road bridges have assumed a key role in recent years due to the strategic importance of these structures for local territories and their exposure to natural and anthropogenic risks. Many assets are in a state of degradation due to adverse environmental conditions, unforeseen loads in the design phase, and lack of maintenance, with often dramatic consequences. In response to these critical issues, integrated approaches based on the exploitation of different digital technologies are emerging to support inspection, monitoring, and maintenance activities. This paper proposes a digital workflow for bridge inspection management, based on the integration of information modeling, online databases, and automated data exchange and updating. The designed workflow enables the creation of a dynamic information model that evolves with the time-dependent data collected during periodic inspections by means of a Visual Programming Language. The data, stored in an online database, are filtered, analyzed, and dynamically associated with model elements, ensuring consistency, traceability, and reduction in manual input errors. The workflow was validated through a field application to an existing bridge, demonstrating its effectiveness in automating information management and providing the basis for the development of an interoperable and scalable platform for the digital management of infrastructure assets.

Abstract While machine learning (ML) has advanced image‐based damage detection, a critical gap remains: the automated translation of detected damage into standardized condition ratings used in structural assessments. Most existing approaches stop at semantic segmentation, overlooking the damage rating step essential for practical inspections. This paper presents a semiautomated system that bridges this gap by linking multi‐label damage segmentation with condition rating prediction. Our contributions are: (1) a data‐driven label taxonomy for damage segmentation, derived from statistical and semantic analysis of 2.2 million inspection records, and designed to support downstream condition rating; (2) a pipeline for converting textual inspection records into structured training data for automated condition rating, and a set of custom bidirectional long short‐term memory (LSTM) models achieving up to F1‐score on this task; and (3) a reference system architecture integrating image segmentation and text‐based damage rating within an interactive 3D inspection interface. The system demonstrates how integrating damage detection and condition rating within an interactive 3D interface can streamline inspection documentation and enhance decision support for concrete structures. Developed in compliance with German inspection standards and designed for adaptability, the system architecture offers a transferable framework for embedding ML‐based automation into digital inspection workflows, ensuring that all components, from damage detection to condition rating, are aligned in an end‐to‐end process.

Abstract High‐resolution (HR) imaging technology is increasingly employed to capture crack images in civil infrastructure, which is vital for ensuring the safety of the bridge inspection process conducted via unmanned aerial vehicles (UAVs). Such applications require the development of advanced algorithms for the segmentation of HR images. Traditional deep learning‐based segmentation methods for inferencing HR images consume considerable GPU resources, which prompts the authors to draw inspiration from the cost‐effective rendering technique in computer graphics and try to apply this advanced method to the refined segmentation of HR crack images. However, the original rendering method, designed to guide rendering points by the coarse segmentation masks, often inadequately directs rendering points towards the crucial boundary areas of tiny cracks, leading to unclear or missing boundary predictions. To address this, an innovative rendering technique was proposed, utilizing probability maps to precisely direct rendering points towards crack boundaries and tiny‐crack branches during inference. This method enhances the accuracy of crack boundary segmentation and reduces the miss rate of tiny crack branches from HR images, all while conserving computational resources. Through model parameter experiments and ablation studies, the optimal model was obtained, and the effectiveness of the improved components was demonstrated. Furthermore, the field test has confirmed that, equipped with the proposed point rendering technique, the UAV is permitted to effectively perform crack inspection within a 3‐m distance from the main beam. Compared to traditional low‐resolution semantic segmentation methods, the UAV bridge inspection time is significantly reduced by 50% while maintaining the same accuracy.

Summary Regulatory compliance in transportation infrastructure asset management (TIAM) is hindered by aging assets, disjointed oversight, and changing policies. Traditional ways of inspecting and reporting by hand don't work well when you need to do a lot of them. This paper examines recent developments in Artificial Intelligence (AI) aimed at enhancing compliance via automation, real-time analytics, and decision support. We highlight techniques like natural language processing (NLP) for parsing regulatory texts and graph neural networks (GNNs) for modeling asset interdependencies. These are based on peer-reviewed studies on AI applications in transportation that were published between 2015 and 2025. For example, Graph SAGE and other GNNs have shown that they can accurately predict road accidents with less than 22% mean absolute error on traffic datasets [3]. Case studies of U.S. bridge inspections using AI-enabled digital twins show that labor costs can be cut by 20–30% and that each project could save up to $15 million [4, 5]. To deal with interpretability, explainable AI (XAI) methods find a balance between accuracy and openness. However, more complicated models often give up performance for less clarity [8]. Federated learning enables privacy-preserving model training utilizing distributed infrastructure data [9]. Climate simulations based on SSP2-4.5 scenarios show that AI can make the road network more resistant to floods, affecting as much as 13.1% of segments [11]. This work encourages reliable AI for long-term TIAM governance.

State Departments of Transportation (DOTs) face challenges with traditional bridge inspections that are time-consuming, inconsistent, and paper-based. This study focused on an existing research gap regarding automated methods that streamline the bridge inspection process, prioritize maintenance effectively, and allocate resources efficiently. Thus, this paper introduces a digitalized bridge inspection framework by integrating Building Information Modeling (BIM) and Business Intelligence (BI) to enable near-real-time monitoring and digital documentation. This study adopts a Design Science Research (DSR) methodology, a recognized paradigm for developing and evaluating the innovative SmartBridge to address pressing bridge inspection problems. The method involved designing an Autodesk Revit-based plugin for data synchronization, element-specific comments, and interactive dashboards, demonstrated through an illustrative 3D bridge model. An illustrative example of the digitalized bridge inspection with the proposed framework is provided. The results show that SmartBridge streamlines data collection, reduces manual documentation, and enhances decision-making compared to conventional methods. This paper contributes to this body of knowledge by combining BIM and BI for digital visualization and predictive analytics in bridge inspections. The proposed framework has high potential for hybridizing digital technologies into bridge infrastructure engineering and management to assist transportation agencies in establishing a safer and efficient bridge inspection approach.

This study quantifies shear and flexural stiffnesses and their changes over time to support structural health monitoring of in-service bridge superstructures across four girder types: reinforced concrete (RC) beams, prestressed concrete (PSC) girders, steel girders, and ultra-high-performance concrete (UHPC) sections, using field ambient vibration testing. A total of 20 bridges across Georgia and Iowa are assessed, involving over 100 hours of on-site data collection and traffic control strategies. Results show that field-measured natural frequencies differ from theoretical predictions by average of 30–35% for RC, and 20–25% for PSC, 15–25% for steel and 2% for UHPC, reflecting the complexity of in situ structural dynamics and challenges in estimating material properties. Site-placed RC beams showed stiffness reduction due to deterioration, whereas prefabricated PSC girders maintained consistent stiffness with predictable variations. UHPC sections exhibited the highest stiffness, reflecting superior performance. Steel girders matched theoretical values, but a span-level test revealed that deck damage can reduce frequencies undetected by localized measurements. Importantly, vibration-based measurements revealed reductions in structural stiffness that were not apparent through conventional visual inspection, particularly in RC beams. The research significance of this work lies in establishing a portfolio-based framework that enables cross-comparison of stiffness behavior across multiple girder types, providing a scalable and field-validated approach for system-level bridge health monitoring and serving as a quantitative metric to support bridge inspections and decision-making.