Articles
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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
Mechanochemical activation of metallic lithium for the generation and application of organolithium compounds in air
Here we report a mechanochemical method for the direct generation of organolithium reagents from readily available organic halides and unactivated lithium metal (lithium wire) under bulk-solvent-free conditions. These reactions rapidly generate a diverse array of organolithium compounds at room temperature without special precautions against moisture and without temperature control.
Kondo Keisuke, Koji Kubota, and Hajime Ito
The Detail Matters: Unveiling Overlooked Parameters in the Mechanochemical Synthesis of Solid Electrolytes
“Simply mixing the reagents by hand for a short time in a mortar and pestle before mechanochemical synthesis dramatically improves the performance of the solid electrolyte.
Hand mixing changes the crystallization behavior, improving the ionic conductivity of the solid electrolyte by up to an order of magnitude.
This discovery will accelerate the search for efficient and logical new electrolyte materials, and ultimately the development of all-solid-state batteries.”Abdulkadir Kızılaslan, Mustafa Çelik, Yuta Fujii, Zheng Huang, Chikako Moriyoshi, Shogo Kawaguchi, Satoshi Hiroi, Koji Ohara, Mariko Ando, Kiyoharu Tadanaga, Saneyuki Ohno, and Akira Miura
Characterizing the Settlement of Activated Sludge Based on AI-Assisted Analysis of Moving and Still Images
In the final process of wastewater treatment, the settleability of activated sludge, a mass of microorganisms responsible for the adsorption and decomposition of pollutants, is important. In order to contribute to the improvement of the efficiency of wastewater treatment plants in rural areas and developing countries, which have problems in terms of economical human resources, this study proposed a low-cost settleability diagnosis method that uses AI-based technology to analyze still images of activated sludge using inexpensive digital microscopes and moving images of activated sludge settling using smartphone camera functions.
Yuki Nakaya, Kai Sugino, Shota Ishizaki, Reiko Hirano, Shuhei Noda, Shinobu Moniwa, Yukio Hiraoka, and Hisashi Satoh
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