Utilization of Unmanned Aerial Vehicle, Artificial Intelligence, and Remote Measurement Technology for Bridge Inspections

Abstract

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.

UAV-based inspection of bridge and tunnel structures: an application review

“…Another example is the acoustic inspection application, performed by the custom-built UAVs developed in Chun et al [109], Mason et al [144], and Moreu et al [145], whose onboard hammering device systems were developed to perform both impact and sound recording for post-processing analyses of concrete structure assessment. Besides that, the implementation of onboard manipulators on UAVs, both 1 Degree-of-Freedom (DoF) ones (explored in Ikeda et al [124] and Ichikawa et al [126]) and 3 DoF ones (in Jimenez-Cano et al [123] and Ikeda et al [125]), may not only be employed for this kind of test, but also for other contact based inspection applications, where a specific device may be manipulated by these UAVs.…”

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

“…These machine learning methods have been widely used in the civil engineering field in recent years. Notable studies include research on crack detection in concrete (Chun et al., 2021; Deng & Lee, 2020; Jiang & Zhang, 2020) and asphalt pavements (Maeda et al., 2018, 2021), bridge inspection (Luo et al 2021; Chun et al 2020), and in the detection of defects and leaks in tunnels (Huang et al., 2018; Xue & Li, 2018). In addition, several researchers have proposed utilizing robots including unmanned aerial vehicles to obtain images from which cracks are detected (Jang et al., 2021; Liu et al., 2020).…”

A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage

“…The aging of bridges has become a growing concern in countries where bridges nearing the end of their designed service life are in use. For example, Japan's Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) reports that by 2028, half of the bridges in the country will be over 50 years old and will have started deteriorating and that by 2033, two‐thirds of all bridges will be affected by deterioration (Chun, Dang, et al., 2020; Ministry of Land, Infrastructure, Transport & Tourism, 2018). Additionally, according to the ASCE infrastructure report card published in 2017, approximately 40% of the 614,387 bridges in America are over 50 years old (ASCE, 2017).…”

“…Further, research has been conducted on evaluating the soundness of structures using vibration (Avci et al., 2021; Chun, Yamane, et al., 2020; Gutierrez Soto & Adeli, 2019; Luo et al., 2020; Rafiei & Adeli, 2017, 2018a; Shrestha & Dang, 2020) and on assessing the internal damage to concrete using nondestructive testing (Chun & Hayashi 2021; Chun, Ujike, et al., 2020; Erdal et al., 2018; Sirca et al., 2018). In addition, research has been conducted on the evaluation of the mechanical performance of corroded steel members (Chun et al., 2019; Karina et al., 2017; Luo et al., 2021; Wu et al., 2021; S. Xu et al., 2019; J. Xu et al., 2020), material and mechanical properties of concrete structures (Feng et al., 2020; Le & Le, 2021; Okazaki et al., 2020; Rafiei et al., 2017), and seismic resistance and post‐earthquake disaster health (Liang, 2019; Nagatani et al., 2021; Perez‐Ramirez et al., 2019). Cost estimation (Rafiei & Adeli, 2018b) based on deep learning techniques too has been explored.…”