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Threshold assessment of gear diagnostic tools on flight and test rig data [microform] / Paula J. Dempsey, Marianne Mosher and Edward M. Huff

Wed, 23 Feb 2005 22:34:34 +0900

Threshold assessment of gear diagnostic tools on flight and test rig data [microform] / Paula J. Dempsey, Marianne Mosher and Edward M. Huff. NASA/TM ; 2003-212220

鋼構造部材の損傷検知への打音検査の適用 / ユー, イーラン

Wed, 18 Feb 2026 13:26:07 +0900

ユー, イーラン. 鋼構造部材の損傷検知への打音検査の適用. Kyoto University, 2024-03-25. DOI:10.14989/doctor.k25258

振動を用いた部分空間法による橋梁構造物の損傷検知 / チョウ, ライ

Wed, 18 Feb 2026 14:10:30 +0900

チョウ, ライ. 振動を用いた部分空間法による橋梁構造物の損傷検知. Kyoto University, 2025-09-24. DOI:10.14989/doctor.k26277

移動点検車両による路面形状同定および橋梁異常検知 / 長谷川, 聡一朗

Wed, 18 Feb 2026 13:50:18 +0900

長谷川, 聡一朗. 移動点検車両による路面形状同定および橋梁異常検知. Kyoto University, 2024-09-24. DOI:10.14989/doctor.k25594

フレキシブルライザーの応答予測と損傷評価に関する研究 / リベロスへレス, カルロスアルベルト

Mon, 16 Mar 2026 18:03:14 +0900

リベロスへレス, カルロスアルベルト. フレキシブルライザーの応答予測と損傷評価に関する研究. Kyoto University, 2008-09-24. DOI:10.14989/doctor.k14135

知的構造用光ファイバひずみセンサに関する研究 / 越出, 慎一ほか

Tue, 02 Jun 2026 10:03:42 +0900

越出, 慎一; Koshide, Shinichi. 知的構造用光ファイバひずみセンサに関する研究. 航空宇宙技術研究所, 1997-03

地上レーザースキャンデータを用いた鉄筋コンクリート建築物の損傷検出フレームワーク / 邵, 万朋

Mon, 27 Oct 2025 23:17:27 +0900

邵, 万朋. 地上レーザースキャンデータを用いた鉄筋コンクリート建築物の損傷検出フレームワーク. 2023-12-27. DOI:10.18997/0002000371; 10.18997/0002000371
Damage assessment is critical important for post-earthquake buildings, especially for the reinforced concrete buildings. It is first to know and detect local damage of damaged buildings (damage types, locations, etc). Normally, visual inspection is conducted for investigation of post-earthquake buildings. However, it need a significant amount of time and manpower to survey a city-level damaged buildings, as well as the high risk of collapse such as aftershocks.Terrestrial laser scanning (TLS), a non-contact measuring instrument, can capture detailed shape of the target building in point clouds with an error of a few millimeters at certain distance. So it is possible to confirm local damage such as concrete spalling and cracking by visualizing the damaged building in 3D point cloud. However, even we have damaged buildings in point clouds, we still need to manually label local damage of them by visual inspection, which is time consuming and have a risk of overlooking local damage in a citywide damaged buildings.To detect local damage of the building surface in 3D point clouds quickly and accurately, automatic damage detection is needed. Recently, learning networks has achieved huge success in classification of various 3D models. If the deep learning technique can be applied for damage detection, which will help us to detect local damage of the building surface quickly and accurately. Although various deep learning methods have been studied for deep learning using 3D point clouds, direct learning from point clouds like PointNet, PointNet++ without preprocessing of the 3D point clouds has become the mainstream method. Therefore, the objective of this study is automatically detecting the visually observable damage in the reinforced concrete building in point clouds by 3D deep learning methods.For automatically detecting local damage of reinforced concrete building in TLS point clouds, our research is divided into 3 parts:・Application of 3D DNN for damage detection・Improvement of damage detection by 3D DNNs investigation・Multi-type damage detection with color information by 3D DNNFor application of 3D DNN for damage detection: We solve the local damage detection problem not as a detection problem but as a classification problem. Before feeding the damaged building into 3D DNN, the whole building surface is divided into small grids. Then, all samples are classified by 3D DNN one by one. As a deep neural network to classify the voxel grids, we used PointNet++. Then, we apply our damage detection model to detect “spalling” areas of a post-earthquake building model at first. In order to detect more “spalling” areas, we propose a shifting technique. With shifting, 92.9% of “spalling” samples can be correctly classified. After that, we also apply our damage detection model for a damaged building on Hashima island. Even 94.8% of “spalling” samples are correctly detected, the precision is considerably low with only 21.5%. The reason is “spalling” areas of damaged building on Hashima island look as smooth as than “intact” ones due to years of corrosion. Any uneven surface on the wall could potentially be identified as “spalling”, which is undistinguishable.To improve the damage detection rate, we investigate the utility of various 3D deep neural networks for “spalling” detection of the damaged building. We compare the performance of different kinds of 3D DNNs for damage detection to find out the most effective 3D DNN for damage detection. Meanwhile, we find a pair of optimal parameters after investigating the impact of different point sizes and voxel sizes on detection results. We demonstrate that PointNet++ SSG with 0.15m voxel size and 1024 sample size can achieve the best performance in our study.For multi-type damage detection with color information by 3D DNN, in the real scene, after earthquake, the RC building will show two types of local damage: crack and spalling. We firstly apply our damage detection model for multi-type damage detection of the post-earthquake building model. Experimental results show that it is still challenging to differentiate “crack”, “spalling” from “intact” samples only based on TLS geometry data. By comparison, 88.7% of the “crack” and “spalling” areas of the damaged building in TLS point clouds with color can be correctly detected by the 3D DNN. For some misclassified cases, we also propose a four-step post-processing method based on cluster analysis including removal of isolated damaged clusters, dilation, and filling, etc. to develop the detection results. Finally, the detection ratio increase to 92.8% after the post-processing.However, when using the TLS to measure a target building with glass components. Reflection noise points will be produced at the opposite of the glass object and mixed with the real-world points. To prevent damage detection by 3D DNN from being affected by noisy points, these noisy points should be removed at first.For reflection removal of RC building in TLS measurement, our study is separated into three steps:(1) For glass objects extraction: we apply an unsupervised segmentation method (super points graph) based on the intensities and geometric features to partition the whole scene into many simple geometric shapes with similar features. Then, glass objects are extracted by modelling the distribution of the average intensities of all segments and a sequence of constraints.(2) For affected points detection: we find the points that fall in affected areas based on the laser scanner position and the four corner points of every glass component.(3) For noisy points detection: we detect the reflection noise points based on the final scores computed by the reflection symmetry and geometric similarity. Experimental shows around 90% reflection noise can be selectively removed.This paper proposes and demonstrates the effectiveness of the 3D DNN for damaged detection of the post-earthquake building. Moreover, our reflection noise removal method can selectively delete most of the noisy points reflected by glass windows and doors without deleting real-world point as much as possible.

モーダルパラメータベースのFEモデルアップデートによる実際の橋の構造性能評価 / シュウ, キン

Sat, 07 Mar 2026 14:52:03 +0900

シュウ, キン. モーダルパラメータベースのFEモデルアップデートによる実際の橋の構造性能評価. Kyoto University, 2022-03-23. DOI:10.14989/doctor.k23858; 10.14989/doctor.k23858

故障脚を有する多脚型ロボットの自己復帰手法 / Chattunyakit, Sarun

Thu, 30 Oct 2025 19:45:31 +0900

Chattunyakit, Sarun. 故障脚を有する多脚型ロボットの自己復帰手法. Hokkaido University, 2019-06-28. DOI:10.14943/doctoral.k13707; 10.14943/doctoral.k13707
Due to the exibility of multi-legged robots, they can be applied in several applications in which regular wheel-based robots are not suitable to perform. However, the multi-legged robots require a number of sensors and actuators. In a normal case, legged robots can perform properly with a controller programmed by the user, but there are some failures occurring when some sensors and actuators are no longer available. The legged robots are nonfunctional after getting damaged since the prior control strategies cannot be employed to operate e ciently with transferred models. A self-recovery method can overcome this problem by nding alternative behavior of the robot. Although these techniques make robots possible to walk after damaging, the existing methods require a lot of time to operate. This research mainly focuses on developing the algorithm for self-recovery, concerning about the learning time and complexities. Not only the self-recovery method is studied in this research, but also other factors that can help the robot to be able to move again. The novel structure model of the quadruped robot has been proposed in this study. The caterpillar-inspired quadruped robot (CIQR) is developed to imitate the caterpillar crawling locomotion when the robot has a small number of active legs. The caterpillars' proleg is added on the robot limb to improve the ability to move after some parts of the robot got damaged. This lets the legged robot become movable even if it has only one leg. However, the structure of legs has to be designed circumspectly due to the face that the proleg can limit the reachable space of robots leg while operating with normal quadruped gaits. In this paper, the new shape of the robotic leg is designed with the inspiration of caterpillar and optimized using PSO algorithm. The tness function of PSO is set as the distance that the robot can travel in both crawling and trotting gait. To discover the new model, the PSO-based Leg-loss Identi cation method (PLI) is proposed. The PLI method uses only onboard sensors that let the robot become more versatile. Particle swarm optimization is utilized to optimize the tness function that is set as the resemblance of candidate models and actually damaged the robot. The acoustic-based fault diagnosis for legged robots (AFL) is developed to detect the abnormalities of joints. The sound of servo motors is recorded simultaneously while a multi-legged robot is executed to perform speci c actions. The results show that both proposed methods can detect the fault parts properly with the broken robot in the experiments. Moreover, the development of new bio-inspired locomotion method is conducted to help the legged robot that has a small number of legs to be able to move again after getting damage. The concept is based on the movement of mudskipper in nature. Self-learning mudskipper-inspired crawling method (SLMIC) is proposed in this study. The reinforcement learning method, Q-learning, is integrated to improve the adaptability of locomotion. The results show that the proposed method is feasible to employ a damaged robot that has two legs. According to the results of proposed works, the upshot of this study is the possibility that the methodology used in this study is feasible to implement with the damaged legged robot in a practical situation.

振動計測による橋梁ヘルスモニタリングのためのベイズ的損傷検知 / 五井, 良直

Sat, 07 Mar 2026 14:51:16 +0900

五井, 良直. 振動計測による橋梁ヘルスモニタリングのためのベイズ的損傷検知. Kyoto University, 2018-03-26. DOI:10.14989/doctor.k21080; 10.14989/doctor.k21080

漏洩磁束探傷法を用いたワイヤロープ検査における劣化損傷の評価に関する研究 / 大塚,賢哉

Thu, 30 Oct 2025 22:15:18 +0900

大塚,賢哉. 漏洩磁束探傷法を用いたワイヤロープ検査における劣化損傷の評価に関する研究. 2022-03-25. DOI:10.15006/32665A5840; 10.15006/32665A5840