Article
Construction & Building Technology
Kien Dinh, Nenad Gucunski
Summary: This study aimed to explore factors affecting the detectability of concrete delamination in GPR images, using both synthetic and real data. The analysis revealed that factors such as delamination thickness, material within it, and emitted signal frequency impact the detectability, while the depth of delamination and its position relative to neighboring steel bars may also affect detection results.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Construction & Building Technology
Ruoyu Chen, Khiem T. Tran, Hung Manh La, Taylor Rawlinson, Kien Dinh
Summary: This paper presents a novel application of 2D fullwaveform inversion of ultrasonic SH-waves for detection of delamination and rebar debonding in concrete structures, providing accurate location and characterization of delaminations and identification of rebar debonding. The SH-FWI method offers clearer structural images with more detailed information compared to traditional techniques such as SAFT.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Construction & Building Technology
Airong Chen, Xurui Fang, Zichao Pan, Dalei Wang, Yue Pan, Bo Peng
Summary: This paper demonstrates the experiences in China regarding surface damage inspection and performance evaluation of RC bridges, including major damage mechanisms, inspection practices, and performance evaluation specifications. Through case studies and statistical analysis, China's research and progress in this field are showcased.
STRUCTURAL CONCRETE
(2022)
Article
Mechanics
Jiancheng Gu, Shigeki Unjoh
Summary: This study introduces a methodology for detecting delaminations in concrete structures using infrared thermography, validates its effectiveness through experiments in various weather conditions, and discusses factors affecting the accuracy of the results.
COMPOSITE STRUCTURES
(2021)
Article
Construction & Building Technology
Linh Truong-Hong, Roderik Lindenbergh
Summary: This paper presents a new method for automatically extracting point clouds of bridge structural components' surfaces, contributing to the automatic generation of 3D geometric bridge models and damage identification.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Environmental Sciences
Ye Xia, Xiaoming Lei, Peng Wang, Limin Sun
Summary: This paper proposes an artificial intelligence-based methodology for condition assessment of regional bridges, which includes data integration, condition assessment, and maintenance optimization. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified.
Article
Construction & Building Technology
Ahmed Gouda Mohamed, Ahmed Khaled, Ibrahim S. Abotaleb
Summary: This research aims to develop an integrated framework for inspection and maintenance intervention in reinforced concrete bridges (RCB), leveraging the potential of as-is Bridge Information Modeling (BrIM). It achieves this by converting 2D drawings into a 3D as-is BrIM model, creating a comprehensive bridge inventory, acquiring inspection data using advanced sensing technologies, and modeling structural defects on the as-is BrIM model. This framework greatly enhances the administration of bridge inspection and maintenance procedures, providing a thorough and clear picture of the bridge's current state.
Article
Construction & Building Technology
Babar Nasim Khan Raja, Saeed Miramini, Colin Duffield, Massoud Sofi, Lihai Zhang
Summary: This study investigated the effectiveness of infrared thermography (IRT) in detecting subsurface delamination of bridge deck components not exposed to direct solar radiation. It was found that IRT can effectively detect such delaminations, with detectable thermal contrast development depending on ambient temperature changes and a suitable detection period between 8 am and 4 pm. Additionally, the study showed that the thermal contrast value increases with bridge deck thickness and delamination size.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Automation & Control Systems
Hongjin Wang, Yuejun Hou, Yunze He, Can Wen, Benjamin Giron-Palomares, Yuxia Duan, Bin Gao, Vladimir P. Vavilov, Yaonan Wang
Summary: In this study, a new post processing algorithm called EVBTF-RPHF is proposed for periodic square wave thermographic nondestructive testing (thermographic NDT). By embedding RPHF into the stable low-rank decomposition EVBTF, the algorithm aims to improve the detectability of defects in thermographic NDT using a periodic heat flux with low-rank spatial distribution. The proposed method is verified by theoretical analysis and experimental results show its reliability in detecting defects with a normalized diameter-to-depth ratio as small as 0.9.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Dominik Merkle, Alexander Reiterer
Summary: This paper summarizes the inspection regulation of concrete bridges in Germany according to DIN 1076, and introduces a fast and reliable remote sensing approach to increase efficiency and level of automation. By comparing different sensor systems using passive and active thermography, the study tests and verifies concepts on real concrete bridges in Freiburg, Germany, analyzing damage detection methods.
AUTOMATED VISUAL INSPECTION AND MACHINE VISION IV
(2021)
Article
Construction & Building Technology
Quang Tai Ta, Van Ha Mac, Jungwon Huh, Hong Jae Yim, Quang Huy Tran
Summary: Active infrared thermography (IRT) is effective for detecting and characterizing subsurface defects in concrete structures. The present study investigates the detectability of delaminations in painted concrete (PCo) compared to normal concrete (NCo) structures using square pulse thermography (SPT), a common approach for active IRT. The results show that delaminations in PCo structures are more detectable in infrared images than in NCo structures, and the width-to-depth ratio of a defect in PCo structures affects the detectability during SPT inspections.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Engineering, Civil
Pengyong Miao, Hiroshi Yokota, Yafen Zhang
Summary: The present study utilizes the LSTM model to identify the relationships between bridge structural deterioration grades and potential influencing factors. Testing the model on a database of 3,368 bridges demonstrates that the LSTM model achieves an accuracy of over 80% in predicting bridge deterioration.
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Sandra Pozzer, Francisco Dalla Rosa, Zacarias Martin Chamberlain Pravia, Ehsan Rezazadeh Azar, Xavier Maldague
Summary: This study used numerical simulations to determine the favorable periods for IRT inspections, particularly for concrete structural health monitoring. The results showed that spring and summer are the most suitable periods for IRT inspections.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Civil
Ferhat Akgul
Summary: The study proposes an integrated rating method that combines visual inspection and multiple NDT techniques at network-level and multi-component approach at bridge level. By using multiple linear regression models and statistical analysis, the weights of evaluation criteria were determined, and the NDT ratings were incorporated into a bridge management system for ranking.
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2021)
Article
Automation & Control Systems
Wenbo Jiang, Min Liu, Yunuo Peng, Lehui Wu, Yaonan Wang
Summary: The study proposed HDCB-Net for pixel-level detection of blurred cracks, achieving efficient fast crack detection through a two-stage strategy with a processing time of only 0.64 seconds per image. Adding to that, a public dataset comprising 150,632 high-resolution images was established for crack detection research purposes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Construction & Building Technology
Pang-jo Chun, Isao Ujike, Kohei Mishima, Masahiro Kusumoto, Shinichiro Okazaki
CONSTRUCTION AND BUILDING MATERIALS
(2020)
Article
Robotics
Keiji Nagatani, Masato Abe, Koichi Osuka, Pang-jo Chun, Takayuki Okatani, Mayuko Nishio, Shota Chikushi, Takamitsu Matsubara, Yusuke Ikemoto, Hajime Asama
Summary: The socio-economic challenges facing the construction industry in Japan, such as the COVID19 pandemic, aging population, aging infrastructure, and severe natural disasters, are addressed by the project "Collaborative robots for adaptation of diverse environments and innovation of infrastructure construction." This project, part of the Moonshot project organized by the Japanese Cabinet Office, focuses on the open design approach for robotics, with research plans involving robots that adapt to the environment, multi-modal AI for environment evaluation, and physical AI with dynamic collaboration of multi-robots.
Article
Robotics
Ryosuke Yajima, Shinya Katsuma, Makoto Suzuki, Fumiya Matsushita, Shunsuke Hamasaki, Pang-jo Chun, Keiji Nagatani, Genki Yamauchi, Takeshi Hashimoto, Atsushi Yamashita, Hajime Asama, Kazumasa Ozawa
Summary: A hydraulic excavator avoidance system was developed to automatically detect and avoid underground pipes, enabling safe and efficient excavation operations. Ground-penetrating radar and deep learning techniques were employed, along with autonomous excavation path generation, to avoid collisions with buried pipes.
Article
Chemistry, Analytical
Shiori Kubo, Tatsuro Yamane, Pang-jo Chun
Summary: This study proposes a deep learning-based semantic segmentation method to automatically detect slope failure regions, with the use of data augmentation to improve detection accuracy.
Article
Green & Sustainable Science & Technology
Xianfeng Li, Mayuko Nishio, Kentaro Sugawara, Shoji Iwanaga, Pang-jo Chun
Summary: This study implemented machine learning to analyze slope stability and developed a classification model and a regression model. The classification model achieved a testing accuracy of 0.9222, while the regression model had a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value. Machine learning shows potential benefits in slope stability analysis.
Article
Chemistry, Multidisciplinary
Shiori Kubo, Nobuhiro Nakayama, Sadanori Matsuda, Pang-jo Chun
Summary: This article discusses the deterioration of infrastructure built during Japan's high economic growth period and the need for maintenance and management of these structures. The aim of the study is to establish a system for investigating and assessing deformation points in headrace tunnel walls and to develop a system that automatically accumulates and plots damage locations and distributions.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Xian Tao, Chandranath Adak, Pang-Jo Chun, Shaohua Yan, Huaping Liu
Summary: The coexistence of subtle and long-range anomalies in real-world industrial applications brings challenges for anomaly localization. Existing methods do not consider learning local and global features simultaneously, which leads to inaccurate localization results. Therefore, a hybrid transformer model called ViTALnet is proposed, which leverages fine-grained feature reconstruction. ViTALnet first extracts local discriminatory features using a vision transformer (ViT) and then integrates global attention and a pyramidal architecture for fine-grain anomaly localization.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Civil
Young Jun Nam, Pang-jo Chun, Cheol Ho Bae, Jeong Hyun Kim, Yun Mook Lim
Summary: This study focuses on accurately simulating the tensile performance of corroded specimens, taking into account the variations in mechanical properties along their circumference. By conducting experimental investigations and utilizing a 3D scanning system, the surface condition of the entire corroded pipeline was accurately represented in finite element models, resulting in a comprehensive comparison between the simulation and experiment results.