Parallel Systems for the Bridge Inspection

In recent years, aging of bridges has become a growing concern, and the danger of bridge collapse is increasing. To appropriately maintain bridges, it is necessary to perform inspections to accurately understand their current state. Until now, bridge inspections have involved a visual inspection in which inspection personnel come close to the bridges to perform inspection and hammering tests to investigate abnormal noises by hammering the bridges with an inspection hammer. Meanwhile, as there are a large number of bridges (for example, 730,000 bridges in Japan), and many of these are constructed at elevated spots; the issue is that the visual inspections are laborious and require huge cost. Another issue is the wide disparity in the quality of visual inspections due to the experience, knowledge, and competence of inspectors. Accordingly, the authors are trying to resolve or ameliorate these issues using unmanned aerial vehicle (UAV) technology, artificial intelligence (AI) technology, and telecommunications technology. This is discussed first in this paper. Next, the authors discuss the future prospects of bridge inspection using robot technology such as a 3-D model of bridges. The goal of this paper is to show the areas in which deployment of the UAV, robots, telecommunications, and AI is beneficial and the requirements of these technologies.

Corrosion causes irreversible damage to metal structures and is one of the most common causes of bridge failure. The advent of affordable unmanned aerial vehicles (UAVs) that can carry digital cameras close to a structure presents an opportunity to quickly inspect bridges for corrosion. However, imagery-based corrosion assessment techniques must be accurate, detailed, and comprehensive to be effectively used in bridge inspections. This paper reviews and evaluates the use of imagery data and machine learning for corrosion assessment on bridges, analyzing existing research on imagery-based corrosion assessment, examining the ongoing difficulties of proposed data analysis approaches, and determining the remaining gaps in the literature. This paper evaluates the existing work with an emphasis on image processing methods; the environment and conditions in which the methods have been tested; evaluation of metrics used for performance assessment; and distinctions between corrosion detection, localization, and segmentation, as are significant in supporting bridge inspection decisions. The analysis finds that image processing techniques are typically too prone to false positives to perform well on chaotic data, particularly with nonuniform lighting and misleading objects in the image, which would be collected during bridge inspections using UAVs. Deep learning algorithms have been reported to perform more accurately than the human benchmark on similar data, and multiple researchers have developed deep learning algorithms that work well on UAV-collected data. However, there remain gaps in performance between methods tested on laboratory data compared to field data and on human-collected compared to UAV-collected data. Algorithms need to be trained on larger, more variable data sets to demonstrate generalizability across bridge inspection applications. If addressed, these methods could improve the safety, time-, and cost-efficiency of bridge inspections; help inspectors understand the state of each asset; and support structural repair, reconstruction, and prioritization decisions before failures occur.

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

Unmanned Aerial Vehicles (UAVs) have garnered significant attention in recent years due to their unique features. Utilizing UAVs for bridge inspection offers a promising solution to overcome challenges associated with traditional methods. While UAVs present considerable advantages, there are challenges associated with their use in bridge inspection, particularly in ensuring effective data collection. The primary objective of this study is to tackle the challenges related to data collection in bridge inspection using UAVs. A comprehensive method for pre-flight preparation in data collection is proposed. A well-structured flowchart has been created, covering crucial steps, including identifying the inspection purpose, selecting appropriate hardware, planning and optimizing flight paths, and calibrating sensors. The method has been tested in two case studies of bridge inspections in the State of New Mexico. The results show that the proposed method represents a significant advancement in utilizing UAVs for bridge inspection. These results indicate improvements in accuracy from 7.19% to 21.57% in crack detection using the proposed data collection method. By tackling the data collection challenges, the proposed method serves as a foundation for the application of UAVs for bridge inspection.

Today, robots are expected to be used for the inspection of infrastructures such as bridges. One important task in bridge inspection is to acquire clear images of its various parts for further damage evaluation. However, this task has not yet been completely realized because of the complexity of bridge structures. For this situation, a UAV with a passive rotating spherical shell (PRSS) that can easily maneuver while protecting itself is introduced. In this paper, we explain the development of the PRSS unmanned aerial vehicle (UAV) based on the design strategy. We then analyze its performance through simulated bridge inspection. Finally, we discuss the results of actual bridge experiments. The spherical shell has a size ( 0.95 m) and structure (fullerene) that is well‐suited for bridge inspection applications. It can also handle an impact equivalent to 2 m/s, which is more than the UAV's maximum flight speed. Test flight experiment also validated the characteristic of PRSS that shows a stable flight during disturbances. The image test also shows that the visual system can provide a full overhead view of the bridge. Likewise, it can detect a 0.1‐mm wide line that replicates the damage (e.g., a crack) from a position 0.5 m away and while the camera moves at 0.3 m/s. These characteristics have yielded a system that can acquire inspection images from critical parts of the bridge. An evaluation by third parties confirmed the effectiveness of the system. Further, the system satisfies the mandatory requirements of the Next Generation Robots for Social Infrastructure (NGRSI) program.

Traditional bridge inspection methods, such as visual inspections and basic non-destructive tests, remain foundational but face limitations due to labor intensity and dependency on inspector expertise, often resulting in inconsistencies. Machine learning (ML) tools offer transformative solutions by enabling the analysis of large datasets to enhance damage detection accuracy, optimize maintenance schedules, and improve resource allocation. The objective of this study was to explore the integration of advanced inspection tools, such as Unmanned Aerial Vehicles (UAVs), digital imaging, and fiber optic sensors, with ML models to enhance data accuracy, decision-making, and inspection rating efficiency. A state-of-the-art review is conducted in this study on the application of ML techniques in bridge inspection and maintenance, encompassing 60 articles and reports published in the last decade. The results of this study show that ML’s ability to integrate advanced inspection tools to improve data accuracy, decision-making, and inspection efficiency. However, the existing challenges persist in data quality, model generalization, and the need for standardization approaches across diverse bridge conditions. The review provides insights into current methodologies, benefits, limitations, and future directions, emphasizing ML’s pivotal role in modernizing bridge health monitoring for a safer and more sustainable infrastructure network. The findings of this study are expected to assist transportation agencies in planning and operating predictive maintenance strategies, focusing on damage detection, maintenance optimization, and resource allocation.

Unmanned aerial vehicles (UAVs) are increasingly leveraged to perform infrastructure inspection tasks, especially with the support of rapidly evolving AI algorithms and hardware in recent years. While the integration of UAV and AI techniques enhances the efficiency and effectiveness of infrastructure inspection, it also raises security concerns due to the potential vulnerabilities existing in the underlying AI models. In this paper, we propose to investigate and discover these vulnerabilities with the case study on bridge inspection. In particular, we designed a two-stage approach that can construct effective adversarial perturbations that make the UAV miss the detection of risk-prone regions during the inspection. Spatial constraints, physical limits, as well as dynamic environmental changes are taken into consideration in our approach to make it practical in the physical world. We evaluate our approach using the COCO-Bridge dataset. Our experimental results demonstrate the effectiveness of our approach in both white-box attack and black-box attack settings.

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

The rapid development of surveillance technology is readily apparent, particularly in monitoring vast and remote locations that present difficulties for human accessibility. Within the realm of contemporary surveillance methods, the utilization of Unmanned Aerial Vehicles (UAVs) has garnered a considerable amount of interest. The swift advancements in science and technology have led to the progressive incorporation of Artificial Intelligence (AI) technologies, particularly in tasks such as monitoring and reconnaissance. The results of this research contribute to the creation of a prototype for a UAV capable of conducting autonomous monitoring missions and integrating artificial intelligence technologies for real-time video processing. This study utilized an experimental methodology, and a Raspberry Pi was utilized for artificial intelligence processes integrated with the aircraft controller. During the decisive experiment, the unmanned aerial vehicle UAV could effectively travel to the designated area, reaching an accuracy of 78.6% in its AI processing. ABSTRAK: Perkembangan pesat teknologi pemantauan kini semakin ketara, terutamanya dalam pemantauan kawasan yang luas dan terpencil sukar diakses manusia. Dalam konteks kaedah pemantauan kontemporari, penggunaan Kenderaan Udara Tanpa Pemandu (UAV) telah menarik minat ramai. Kemajuan pesat sains dan teknologi telah menghasilkan gabungan teknologi Kecerdasan Buatan (AI) secara progresif, terutama dalam bidang pemantauan dan peninjauan. Penyelidikan ini memberi sumbangan kepada penciptaan prototaip UAV yang mampu menjalankan misi pemantauan sendiri, disamping gabungan teknologi kecerdasan buatan bagi memproses video masa nyata. Kajian ini menggunakan metodologi eksperimen dan Raspberry Pi yang disepadukan dengan pengawal pesawat bagi proses kecerdasan buatan. Semasa eksperimen penentuan, UAV berjaya bergerak dengan berkesan ke kawasan yang ditetapkan, mencapai ketepatan 78.6% dalam pemprosesan AI.

Early damage detection in bridges is fundamental to their continued safety, and therefore of utmost importance to the bridge managers and policy makers. The traditional visual inspection which is the common practice for bridge inspection is inefficient, time-consuming, costly, risky, subjective and require the expertise of highly qualified inspectors. Consequently, the use of unmanned aerial systems (UASs) has gained significant attention in the area of bridge inspection. Most UAVs are quite expensive, ranging between $8000-$25000 for the best categories. It however requires the use of UAV equipped with high-cost sensors and longer battery duration, and adequate man power for real-time inspection. The expense of the UAV based inspection makes it unaffordable for many African countries. More recently, 3D models are increasingly deployed for image-based damage identification in bridges and other structures. The 3D models are constructed inform of a digital twin, and allow for computerized inspection using low-cost drones. This paper presents 3D Reconstruction approach for damage inspection and condition assessment of a bridge using images from low-cost unmanned aerial vehicle (UAV). The overall approach was illustrated in form of a case study on a precast concrete bridge. The 3D Reconstructed model of the bridge was virtually inspected to detect damages such as cracks, delamination, concrete deterioration, etc. The results showed that 3D reconstruction using low-cost UAV has great potential in its applications in bridge assessment.

Abstract. The Simultaneous Localization and Mapping (SLAM) technology is fundamental to the autonomous navigation of Unmanned Aerial Vehicles (UAVs) and holds significant value for the realization of UAV-based bridge inspections. However, conventional SLAM methods for UAV face challenges related to low continuity and weak reliability across different scenes, making it difficult to meet the requirements for comprehensive bridge localization and mapping. To address the limitations of existing UAV-based SLAM approaches, we propose a seamless SLAM system that integrates IMU, LiDAR, and RTK. In open scenes (such as the top and sides of a bridge), high-precision absolute localization is achieved by fusing IMU and RTK through an iterative error-state Kalman filter (IESKF). In occluded environments (such as the underside of a bridge), an IMU/LiDAR odometry is used to recursively estimate the UAV’s pose. In cross-scene situations (when the UAV passes through a bridge arch), the quality of sensor data is evaluated based on an interactive multi-model (IMM), and an adaptive switching mechanism is employed between two localization modes—IMU/RTK mode and IMU/LiDAR mode—ensuring smooth and seamless multi-source fusion localization even in the presence of sensor signal fluctuations. To validate the effectiveness of our method, extensive tests were conducted on several real-world bridge scenarios. The results show that our method can achieve centimetre-level cross-scene localization accuracy in bridge inspection applications, which indicates its feasibility and effectiveness.

PurposeThis paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.Design/methodology/approachThe research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.FindingsThe data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.Originality/valueThe contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Despite its reputation as one of the least digitized sectors, the construction industry increasingly adopts artificial intelligence (AI) and construction technologies. However, previous research has not adequately explored the benefits of AI techniques and construction technologies nor the correlations between them. This study aims to fill this knowledge gap. To this end, the authors used a multistep research methodology. First, a systematic literature review of studies published between 1985 and 2022 was conducted to identify AI techniques and construction technologies. Second, content and frequency analysis were performed to determine the benefits associated with AI and technology adoption. Finally, a survey was developed, collected, and analyzed to (1) quantify the utilization of AI techniques and construction technologies; (2) assess the benefits of utilizing them; and (3) investigate the correlations between AI techniques, construction technologies, and key benefits. The study identified four AI techniques, 13 construction technologies, and 28 associated benefits. Results showed that widely adopted AI techniques are computer vision and knowledge-based systems (KBS) and the most adopted construction technologies include the global positioning system, building information modeling (BIM), the GIS, and unmanned aerial vehicles (UAVs). The key benefits are increased productivity, improved safety, improved monitoring and control, and informed decision-making. Correlations discovered in the study include (1) increased productivity, strongly associated with blockchain, BIM, and digital twin; (2) improved safety, closely linked with KBS, machine learning (ML), and robotics; (3) enhanced monitoring and control, associated with laser scanning, real-time systems, and UAVs; and, (4) informed decision-making, correlated with ML, natural language processing, and blockchain. This study provides a quantified comprehensive understanding of the benefits of AI and construction technologies, offering insights to prioritize innovations, and helping practitioners make informed investments for a competitive advantage.

The task of bridge inspection is currently carried out by humans, which is time-consuming, costly, and high-risk. There is an urgent need to develop an autonomous system to inspect bridges. Several different approaches have been proposed — Unmanned Aerial Vehicles (drones), and magnetic tracked vehicles — and these solutions lack autonomous driving capabilities. Furthermore, past software solutions, including machine learning, are non real-time solutions and are also limited to locating damage in a single image. This research designs and implements AutoBot: the first integrated, autonomous, wire-ringed, column-climbing robotic system capable of real-time mapping for automatic surface crack detection. Real-time mapping techniques were developed by a combination of two components: YOLO, a single-stage object detection algorithm, and a 3D mapping technique, capable of converting identified cracks into 3D coordinates. Driving was achieved by bang-bang and Proportional Integral (PI) controllers. After tuning on a bridge column model, AutoBot was applied for real-world concrete bridge inspection. The results showed AutoBot successfully climbed a column 1.33 meters in diameter and 5 meters tall at a speed of 4.7 cm/s, completing one cycle of action, climbing and mapping a single meter, in 22 seconds. The detection obtained a mean average precision of 82.6%. Compared to other YOLO algorithms that use the same image test size, AutoBot’s YOLO algorithm had the highest precision, recall, and mean average precision. AutoBot is the first successful demonstration of an autonomous, real-time system capable of inspecting bridges.

Purpose This study investigates the application of image captioning technology in automated bridge inspection. Given the scarcity of research in this domain, this study aims to evaluate the feasibility, effectiveness and practical implications of using transformer-based models to generate natural language descriptions of bridge damage from visual data. Design/methodology/approach A triangulated research methodology was used, comprising a systematic literature review to assess the current state of image captioning in bridge inspection; a feasibility study using an encoder–decoder architecture (EfficientNet–transformers) trained on a structural damage dataset; and an interview-based transferability study with seven industry professionals to evaluate practical adoption challenges. Findings The systematic review identified only four relevant studies, underscoring the nascent state of research in this field. The feasibility study demonstrated promising results, with EfficientNet–transformers achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1:0.944, BLEU-4:0.904) in structural damage description tasks. Finally, industry feedback highlighted potential benefits in inspection efficiency but emphasized challenges in workflow integration and model reliability. Originality/value To the best of the authors; knowledge, this study represents one of the first comprehensive explorations of image captioning for bridge inspection, contributing both methodological and practical insights. It identifies key research gaps, including the need for domain-specific data sets and standardized evaluation frameworks, while proposing actionable directions for future AI applications in infrastructure maintenance. The findings provide a foundation for advancing automated inspection technologies toward safer and more efficient infrastructure management.