Civil Engineering
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Design of micro-electro-mechanical systems-driven environmental sensor for steel corrosion detection in concrete using sacrificial anode metal sheets
This study develops a MEMS (Micro-Electro-Mechanical Systems)-based monitoring system incorporating MEH (Micro-Energy Harvester) technology to identify chloride-induced corrosion-prone areas in RC structures. The proposed system integrates sacrificial anode metal sheets (SAMS) into MEMS–MEH devices, enabling simultaneous corrosion detection and energy harvesting. In tracking corrosion progress through frequency shifts while generating power, the system operates without batteries or external power. The results help define target operating frequencies for reliable MEMS–MEH performance in predicting corrosion conditions in RC structures.
Nicharin Nithimethaporn, Katsufumi Hashimoto, Hiroyuki Mitsuya, Hisayuki Ashizawa, Takuma Ishiguro, and Wanchai Yodsudjai
Application of unsupervised AI-assisted acoustic wave sound analysis for non-destructive detection of steel corrosion induced deterioration
Reinforced concrete structures require reliable monitoring to ensure safety and efficient maintenance. Non-destructive testing methods such as tapping sound inspection are widely applied. However, the diagnosis results often depend on technical expert skill and experience. This study proposes an easy-to-use, AI-based evaluation method for tapping sounds using unsupervised deep learning. Laboratory tests were carried out on reinforced concrete beams with simulated steel bar corrosion. The method proposes an anomaly index that reflects corrosion progress and surface cracking. The results demonstrate that acoustic inspection with AI can support early damage detection and improve condition assessment of concrete structures.
Nopphanan Phannakham, Katsufumi Hashimoto, Yasuhiko Sato, and Naoshi Ueda
A multi-module deep learning framework with graph-based network and crack attention for tunnel lining crack segmentation from LiDAR point cloud
Cracks in railway tunnels threaten safety and require efficient monitoring. This study introduces PointCrackNet, a deep learning method that detects tunnel lining cracks directly from LiDAR point clouds. By combining graph-based convolution, attention mechanisms, and a crack-enhancement module, the model accurately captures fine crack details while maintaining global structural context. A tailored loss function addresses class imbalance and improves crack continuity. Tested on a large-scale LiDAR dataset from a real railway tunnel, PointCrackNet outperformed existing methods. The approach enables automated tunnel inspection and supports smart, data-driven infrastructure maintenance.
Shanpeng Liu, Koichi Isobe, Junling Si, Diyuan Li, and Daoju Ren
Construction and Building Materials Volume 494, 2025, 143383
Effects of Combined Deterioration of Steel Corrosion and Freeze-thaw Cycles on the Pull-out Behavior of Deformed Bars in Concrete
This study is the first to systematically investigate how loading sequence and cracking history affect the bond performance of deformed bars under the combined deterioration caused by steel corrosion and freeze–thaw action, a serious problem in concrete structures in cold regions. Through controlled experiments simulating realistic deterioration paths, the study clarified the influence of damage sequence on structural performance. In particular, it demonstrated that pre-existing cracks significantly accelerate deterioration by promoting moisture ingress and freeze–thaw damage. These findings highlight that considering damage history is essential for reliable durability assessment and long-term maintenance planning of aging reinforced concrete infrastructure.
Muhmudul Hasan Mizan, Ryuhei Hayakawa, and Koji Matsumoto
Mesoscale modeling of anisotropic compressive behavior and pull-out performance of 3D printed concrete with steel bars using 3D RBSM
This study uses a 3D Rigid Body Spring Model (RBSM) to analyze the anisotropic behavior of 3D-printed concrete (3DPC) with steel reinforcement. Validated by experiments, the research highlights how the mesoscale structure—specifically porous interlayer interfaces—affects performance. Results indicate that specimens loaded parallel to the printing direction exhibit superior compressive strength and bond performance. Conversely, loading perpendicular to the layers leads to stress concentrations and weaker bonds due to interfacial zones. Overall, this research provides a predictive framework for optimizing the structural integrity of 3DPC through mesoscale modeling.
Jiaxu Yao, Jie Luo, Minghong Qiu, and Kohei Nagai
Reliable pathfinding problems for a correlated network: A linear programming problem in a hypergraph
Kenetsu Uchida, Yifan Wang, and Ryuichi Tani
European Journal of Operational Research Volume 326, Issue 2, 16 October 2025, Pages 234-254
SH wave scattering in Eringen’s nonlocal elastic solid using the method of fundamental solutions
Eringen’s nonlocal elastic solid is a mechanical model that enables the analysis of phenomena difficult to describe using classical elasticity. This study analyzed wave scattering in nonlocal elastic solids using the method of fundamental solutions, a meshfree numerical method. An analytical representation of the traction operator specific to nonlocal elasticity was derived, and scattering characteristics relevant to ultrasonic nondestructive testing were evaluated.
Akira Furukawa, Taizo Maruyama, Takahiro Saitoh, Sohichi Hirose, Davinder Kumar, Dilbag Singh, and Sushil K. Tomar
Impact-echo for different level cracks detection in concrete with artificial intelligence based on un/supervised deep learning
Aging concrete infrastructure such as bridges and tunnels requires effective inspection to ensure safety and durability, particularly for detecting invisible internal cracks subjected to structural integrity. Impact-echo, which is one of non-destructive testing methods, is widely used but costly and time-consuming with relying on skilled and experienced analysis. This study integrates AI with impact-echo data to improve crack detection. Supervised deep learning using FFT-transformed signals enables accurate classification of multiple crack levels, including intact condition of concrete. However, data labeling for each existing structures is impractical, so an unsupervised approach using an auto-encoder is proposed to identify internal crack levels through anomaly-based indices without labeled data.
Jeero Pandum, Katsufumi Hashimoto, Takafumi Sugiyama, Wanchai Yodsudjai
Research and Development of Anomaly Detection Technology for Civil Infrastructures Using Electret Vibrational Energy Harvesting Device and Wireless Power/Data Transfer
We will develop a battery-less anomaly detection device capable of sensing the condition and environment of infrastructure structures, and establish a wireless energy and data communication platform. In particular, a system will be realized that allows monitoring via an IoT network using microwave spatial transmission (WPDT) technology of information related to structural deterioration, damage, environmental conditions, and faults autonomously detected by electret MEMS sensors powered by environmental vibration energy harvesting. This will enable the social implementation of a seamless monitoring platform targeting infrastructure structures and their auxiliary facilities, capable of phase-free response at all times, including both normal and emergency conditions.
Katsufumi Hashimoto (PI), Hiroyuki Mitsuya, and Teruo Fujiwara
Material Behavior and Mechanical Performance Based on Hierarchical Structure Formation of 3D-Printed Concrete
This study investigates the hierarchical structure of 3D-printed concrete (3DP concrete) by analyzing two key aspects: the microscopic heterogeneity caused by material segregation within the filament during deposition, and the macroscopic non-uniformity resulting from interfacial voids formed along the printing path. By clarifying these higher-order structures, we demonstrate that 3DP concrete possesses multiscale material properties and mechanical behavior, making it a hierarchical material. Furthermore, we establish a systematic academic framework for understanding how heterogeneity (material geometry) and non-uniformity (structural geometry) are embedded as geometric parameters in 3D spatial information, providing insights into the mechanical performance and failure modes of 3DP concrete.
Katsufumi Hashimoto, Takafumi Sugiyama, and Shimpei Ono
Grant-in-Aid for Scientific Research(B) 2025.4.1～2028.3.31