Yoshinobu Oshima

Long-term bridge health monitoring and performance assessment based on a Bayesian approach

Abstract This study presents a damage detection approach for the long-term health monitoring of bridge structures. The Bayesian approach comprising both Bayesian regression and Bayesian hypothesis testing is proposed to detect the structural changes in an in-service seven-span steel plate girder bridge with Gerber system. Both temperature and vehicle weight effects are accounted in the analysis. The acceleration responses at four points of the bridge span are utilised in this investigation. The data covering three different time periods are used in the bridge health monitoring (BHM). Regression analyses showed that the autoregressive exogenous model considering both temperature and vehicle weight effects has the best performance. The Bayesian factor is found to be a sensitive damage indicator in the BHM. The Bayesian approach can provide updated information in the real-time monitoring of bridge structures. The information provided from the Bayesian approach is convenient and easy to handle compared to the traditional approaches. The applicability of this approach is also validated in a case study where artificially generated damage data is added to the observation data.

Mitigating the effects of variable speed on drive-by infrastructure monitoring

Vehicle-based monitoring has the potential to become an accurate and cost-efficient way to monitor infrastructure assets, but a number of challenges must be addressed for such a technique to be implemented widely. The majority of vehicle-based infrastructure sensing has assumed that the vehicle’s speed profile is identical every time it passes over the asset of interest. Ultimately, however this technology will be most practical if damage detection schemes can be applied regardless of the speed of the vehicle. Thus methods must be designed to handle speed variability to make this method more practical. In this paper we investigate the effects of variable speed when monitoring infrastructure from the dynamic response of a passing vehicle, which we measure by placing accelerometers on the vehicle of interest. We have conducted a series of laboratory tests to study this phenomenon, in which a vehicle crosses over a scaled model bridge structure with a varying speed profile. We quantify the ability of several features to detect changes in the infrastructure, independent of the variable speed. We show that aligning signals to normalize for speed variability improves the classification results. This work brings us closer to the ultimate goal of using vehicle-based monitoring to ensure more efficient and more reliable infrastructure in the future.

A STUDY ON EVALUATION OF IRI PREDICTION MODEL FOR CAMBODIAN NATIONAL ROAD PAVEMENT ROUGHNESS

In Asian countries such as Cambodia, road pavement tends to deteriorate faster than expected, and it is important to predict the pavement condition for road maintenance. However, in many developing countries the information used for the deterioration prediction of pavement is not sufficient. Thus in this study, several prediction models are examined using the limited information about the pavement. Herein actual data of IRI (International Roughness Index), FWD (Falling Weight Deflectometer) and traffic measured in all 1-Digit national roads of Cambodia are used to build the models and the models are examined using those data of national road No.5. As a result, it was found that the increase in roughness of pavement is related strongly to the state of roughness itself, and also the prediction model using exponential function exhibits most accurate prediction among the conventional models under limited information. カンボジアをはじめ多くの途上国では，道路舗装の劣化が予想を上回る速さで進展し，場合によっては経済発展を阻害することがある．そのため，交通量など各種要因を考慮した破損量の推移を的確に予測し，効果的な維持管理を行うことが極めて重要である．しかし多くの途上国では，劣化予測に必要な情報が不足しており，このような状況下における劣化予測について検討する必要がある．本研究では，カンボジアにおける主国道である国道1～7号で実施された舗装の実調査データに基づき，IRI（International Roughness Index）の低下過程に及ぼす各種要因の影響を明らかにするとともに，各種劣化予測モデルによる劣化評価を国道5号を対象に試みた．その結果，IRIの低下速度はIRIの値そのものに大きく影響を受けることや，限られた情報下においては，指数関数モデルによるIRIの予測値が最も実測値と近くなることが明らかとなった．