Miyamoto Ayaho

In this study, a novel VR-based system for inspection of deteriorating concrete bridges is proposed to support of the crack and spalling detection which is a key role in existing bridge maintenance. The proposed system, called a VR-based support system for bridge inspection, was applied to an aged bridge for confirming the representing ability of both cracking and spalling damages as a specific example. Experimental results showed that the VR images data with hammering sounds (audio) data have a sufficient ability of representing both crack width over 0.2 mm and a certain spalling area near concrete surface. In this system, the three-dimensional image(photo) data was used to recognize the cracking damage for professional visual inspector. On the other hand, as spalling damage inside the concrete surface couldn’t be identified accurately from only image(photo) data, then the hammering sounds (audio) data was incorporated into the system as an additional function for recoginittion of the spalling damage.

In this study, as one solution to the problem for condition assessment of existing short- and medium-span reinforced/prestressed concrete bridges, a new monitoring method using a public bus as part of a public transit system (bus monitoring system) is proposed, along with safety indices, namely, characteristic deflection, which is relatively free from the influence of dynamic disturbances due to such factors as the roughness of the road surface, and a structural anomaly parameter. A basic study was conducted by using the results of technical verification experiments and numerical analysis simulation. This paper describes the details of not only how to assess the bridge condition by public bus vibration measured in operating on Ube City bus network as a specific example for verify the system but also what kind of consideration we need to apply the system to existing bridges in overseas country. And, the degrees of abnormality of the monitored bridges were expressed with the "inspection necessity level" to enable local governments to prioritize their bridge maintenance needs quantitatively.

This paper describes the methods of performance evaluation and remaining life prediction for an aged reinforced concrete (RC) T-girder bridge via visual inspection data. The Bridge Management System (J-BMS) that was previously developed by the authors, and which is capable of forecasting the deterioration process of existing bridge members, was applied to evaluate the safety indices (soundness score) and re-maining life of the target bridge based on these test results. Using these methods, the remaining life of an aged RC-T girder bridge(SK-bridge) can be quantitatively estimated by applying the bridge rating expert (BREX) system, which is a subsystem of the J-BMS RC version that incorporates with the field inspection data. In this study, visual inspection was carried out on the aged bridge(SKbridge) by professional visual inspectors, during which all variations of the inspection results were evaluated using a five-step question-naire. As a result, it was found that the soundness score (safety index) and remaining life predictions were influenced by the learning (supervised) data selection. Additionally, the predicted remaining lives were verified through concrete core tests extracted from main girders and deck slabs, and cross-section cutting-off girder tests.

This paper introduces an approach to the realization of an ICT-based bridge remote monitoring system which enables real-time monitoring and control for unexpected heavy loads such as big earthquake and strong typhoon. In this paper, the integrated bridge remote monitoring system called the “Intelligent Bridge” consists of a stand-alone monitoring system (SMS) and a web-based internet monitoring system (IMS) for bridge maintenance but also its application to para-stressing bridge system as an intelligent structure. For the purpose of verifying its validity to actual bridge structure, an attempt is made to study the possibility of controlling the actual structural performance by adjusting the cable forces on a 2-span continuous cable-stayed bridge model. The experimental results demonstrate that the implemented monitoring system supplies detailed and accurate information about bridge behavior for further evaluation and diagnosis, and it also opens up prospects for future application of web-based remote system to actual in-service bridges under field conditions.

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10-30m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurements with a public bus as part of a public transit system (called "Bus monitoring system") has been developed to capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been fieldtested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also descrived in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal.

The Japanese Bridge Management System (J-BMS) RC version consists of the following three subsystems: (1) a bridge maintenance database system (J-BMS DB), (2) a concrete bridge rating expert system (RC-BREX) and (3) a maintenance plan optimization system (MPOS). Especially, in this paper, the RC-BREX system for deteriorating concrete bridges is described based on how to construct from a hierarchical neural network in order to carry out fuzzy inference and machine learning by using the neural network with the back-propagation method. Furthermore, the comparisons between diagnostic results by bridge experts and those of the proposed system are presented so as to demonstrate the validity of the system’s learning capability by using the training set for machine learning which obtained from inspection on actual in-service bridges and questionnaire surveys by bridge experts.