Volume 2, 2026
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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
Superfluid dripping: a new analog for continuous time crystals
The dripping behavior of superfluid helium-4 has been found to be consistently discretized, even when flow rates vary. This unexpected phenomenon suggests that the superfluid dripping system exhibits time crystallinity by spontaneously breaking continuous time translation symmetry. The condition for the emergence of this continuous time crystal is that the edges of the pendant droplets, which hang from the underside of the cup, can move freely—a characteristic specific to superfluid dripping. This free motion leads to volume-independent oscillation periods for the droplets, effectively eliminating the influence of fluctuations in the flow rates.
Shota Takamatsu, Ryota Yamane, Tomoyuki Tani, Yuri Ishimoto, Keito Miyake, Yuki Aoki, and Ryuji Nomura
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
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
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
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
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
Effect of flow residence time on the flame-retardant performance of fluorine-based flame retardant: Comparison of blowoff limits of CH₂F₂ and CH₄
The article investigates the combustion characteristics of hydrofluorocarbon (HFC) and hydrocarbon (HC) fuels to understand the increased flammability of fluoropolymers like ETFE under microgravity. Key findings: CH₂F₂ exhibits minimal sensitivity of blowoff limit to oxygen, unlike CH₄. CH₂F₂ flames have lower temperatures and suppressed H and OH radical formation, due to dominant HF-producing pathways inhibiting radical chain reactions. Despite susceptibility to blowoff, CH₂F₂ maintains high adiabatic flame temperature, allowing combustion at low oxygen if sufficient residence time is provided.
Yusuke Konno, Ayuto Ota, Nozomu Hashimoto, and Osamu Fujita
Solid-phase fluorescence excitation-emission matrix spectroscopy of soil, fulvic acid fractions, and clay mineral complexes: Evidence from red shift of fluorescence maxima associated with aggregation
Most of the analysis of natural organic matter (humic substances) in soil is carried out in a solution state by an alkali extraction operation. However, this approach addresses concerns regarding the potential alteration of humic substances during alkaline extraction, which may cause these substances to lose their original structure. In this study, as a non-extraction and non-destructive method, solid-phase fluorescence (excitation-emission matrix) spectroscopy (SPF-EEM) was applied for the first time to a standard humic substance and its complex with clay. It was found that the excitation-emission wavelength could shift according to the state of solution, complex, aggregate, etc.
Yuki Nakaya, Takashi Hirose, Ryuichi Tamori, Nobuhide Fujitake, Satoru Nakashima, Hiroshi Yamamura, and Hisashi Satoh
Development of hydrogen production technology using plasma-assisted water electrolysis
In “contact glow discharge,” an electrolytic reaction where plasma and water come into contact, phenomena where the Faraday efficiency exceeds 1 have been reported, but the reaction mechanism remains unclear. We aim to develop a plasma-driven electrolysis method that generates stable direct-current plasma in water, elucidate the mechanism behind the phenomenon where hydrogen production increases significantly compared to conventional electrolysis, and establish a highly efficient hydrogen production technology.
Grant-in-Aid for Challenging Research (Pioneering) 2025.6.27～2029.3.31