Kiyoyuki Kaito

Development of non-contact scanning vibration measurement system for real-scale structures

In order to rationalize vibration monitoring in structures, this paper addresses a non-contact scanning vibration measurement system employing laser doppler vibrometers. Generally, in the case of measurement for concrete members or dirt-adhering steel members, monitoring by laser doppler vibrometers is extremely difficult due to low laser reflectance. In this study, it is experimentally verified that the return or reflected laser beam quantity depends on subtle differences in the surface condition of a measurement object. Therefore, by searching the vicinity of the insufficient measurement point for the optimum point which provides a maximum reflected laser beam quantity, a remarkable improvement of measuring accuracy for real-scale structures can be achieved. As an example, vibrations of steel girders and reinforced concrete deck of actual bridges are measured, so as to verify the measuring accuracy of the developed system, and eigen local mode shapes of the members are able to be identified.

Deterioration Forecasting Model with Multistage Weibull Hazard Functions

In this paper, a time-dependent deterioration forecasting model is presented. In the model the deterioration process is described by transition probabilities, which are conditional upon actual in-service duration. The model is formulated by the multistage Weibull hazard model defined by using multiple Weibull hazard functions. The model can be estimated based upon inspection data that are obtained at discrete points in time. The applicability of the model and the estimation methodology presented in this paper are investigated against an empirical data set of highway utilities in the real world.

A Bayesian Estimation Method to Improve Deterioration Prediction for Infrastructure System with Markov Chain Model

In many practices of bridge asset management, life cycle costs are estimated by statistical deterioration prediction models based upon monitoring data collected through inspection activities. In many applications, it is, however, often the case that the validity of statistical deterioration prediction models is flawed by an inadequate stock of inspection dates. In this paper, a systematic methodology is presented to provide estimates of the deterioration process for bridge managers based upon empirical judgments at early stages by experts, and whereby revisions may be made as new data are obtained through later inspections. More concretely, Bayesian estimation methodology is developed to improve the estimation of Markov transition probability of the multi-stage exponential Markov model by Markov chain Monte Carlo method using Gibbs sampling. The paper concludes with an empirical example, using the real world monitoring data, to demonstrate the applicability of the model and its Bayesian estimation method in the case of incomplete monitoring data.

Detection of structural damage by ambient vibration measurement using laser Doppler vibrometer

In order to rationalize structural maintenance, this paper focuses on vibration characteristics as indices to detect damage, and addresses advanced vibration measurement system and damage detection method based on changes in vibration characteristics. First of all, vibration measurement system using Laser Doppler Vibrometer, which can scan the objective structural surface, is developed and an identification method from laser ambient vibration measurement is proposed. Next, a damage detection method, which calculates mass and stiffness changes in reverse based on changes in mode shapes, is also constructed. These methods show their validity experimentally through vibration measurement for a steel plate before/after damage. Furthermore, to be applied for real civil structures which possess low laser reflection, the laser vibration measurement system is advanced with adding a function which can automatically search the maximum points of laser reflection. By means of this advanced laser vibration measurement system, vibration measurement on a reinforced concrete deck is carried out and its local mode shapes are identified.

The Eigensystem Realization Algorithm for ambient vibration measurement using laser Doppler vibrometers

Laser Doppler vibrometers (LDVs) used in ambient vibration measurement have eliminated the expense of using sensors and exciters, and the need for a closing service of the structure during the measurement period. Since there are only two laser sources available for measuring responses at two points, the data acquired from all over the structure to provide information on its modal parameters will not be synchronous, leading to the fact that many current modal analysis identification techniques will not be applicable. Introduced in this paper is a technique to synchronize response measurements from the LDV, then feed them to the Eigensystem Realization Algorithm (ERA) to obtain the structures modal parameters. Two experiments have been conducted on a steel plate to verify the technique. It has been found that the modal parameters can be successfully identified and that a change in these parameters is prominent when the system properties are changed. This finding is significant in structural health monitoring, in particular for damage detection, in that it can provide a fast, accurate, and relatively cheap routine to pinpoint the location of the damage.

Big data-based deterioration prediction models and infrastructure management: towards assetmetrics

Abstract In the past decades, infrastructure management has been performed based on implicit knowledge, consisting experience and knowledge of professional engineers. The objective of assetmetrics is to convert such decision processes based on implicit knowledge and experience into systematic decision processes based on formal knowledge. The presented research and development policy is a practical approach that tries to create methodologies based on data obtained through daily and periodic inspections. Moreover, the authors point out that in the field of asset management, technical knowledge related to the analysis of existing data is more important than hardware technologies for obtaining new data. The authors also discuss these ideas as pertaining to the concept of big data. Finally, by presenting examples of advanced researches on assetmetrics, the authors give an overview of monitoring methodologies and comprehensive risk management in the relevant field.

Deterioration forecasting of joint members based on long-term monitoring data

To compensate for shortcomings of visual inspection data- based asset management, monitoring data-based asset management has attracted a lot of attention. However, there are few researches to detect abnormalities and extract the progress of deterioration based on long-term monitoring data. In this study, the authors express the time series data obtained through long-term monitoring by the autoregressive moving average with exogenous variables generalized autoregressive conditional heteroskedasticity (ARMAX-GARCH) model, and develop the efficient method to estimate unknown parameters based on Bayesian method. Then, a method to forecast the timing of detailed inspection utilizing the ARMAX-GARCH model is developed. Lastly, this methodology is applied to the data of long-term monitoring targeted at the joint members of viaduct, to evaluate its effectiveness.

Obstacle Emergence Risk and Road Patrol Policy

The authors model the emergence processes of road obstacles, such as fallen objects on roads, the deformation and destruction of pavements, and the damage and destruction of road facilities, as counting processes. Especially, in order to take into account the heterogeneity of the emergence risk of a variety of road obstacles, the authors model a mixture Poisson process in which the arrival rate of road obstacles is subject to a probability distribution. In detail, the authors formulate a Poisson-Gamma model expressing the heterogeneity of the arrival rate as a Gamma distribution and formulate the management indicator of the emergence risk of road obstacles. Then, a methodology is developed in order to design a road patrol policy that can minimize the road obstacle risk with a limited amount of budget. Furthermore, the authors empirically verify that it is possible to design road patrol policy based on the emergence risk of actual road obstacles with the proposed methodology, by studying the cases of the application of the methodology to general national roads.

A Mixed Prediction Model of Ground Subsidence for Civil Infrastructures on Soft Ground

The estimation of ground subsidence processes is an important subject for the asset management of civil infrastructures on soft ground, such as airport facilities. In the planning and design stage, there exist many uncertainties in geotechnical conditions, and it is impossible to estimate the ground subsidence process by deterministic methods. In this paper, the sets of sample paths designating ground subsidence processes are generated by use of a one-dimensional consolidation model incorporating inhomogeneous ground subsidence. Given the sample paths, the mixed subsidence model is presented to describe the probabilistic structure behind the sample paths. The mixed model can be updated by the Bayesian methods based upon the newly obtained monitoring data. Concretely speaking, in order to estimate the updating models, Markov Chain Monte Calro method, which is the frontier technique in Bayesian statistics, is applied. Through a case study, this paper discussed the applicability of the proposed method and illustrated its possible application and future works.

Identification of Dynamic Properties of Open-Deck Viaducts Under Passing Train Loads

When planning the introduction of high-speed trains on existing viaducts, it is important to grasp the resonance phenomenon by measuring the vibration of actual bridges and understand this phenomenon from the engineering perspective. In this study, the author focused on the open-deck viaducts in snowy cold regions, where high-speed trains will be introduced, carried out vibration monitoring of actual bridges utilizing the passing train loads and identified their dynamic properties with ERA (Eigensystem Realization Algorithm). When identifying dynamic properties, the first bending mode and the first torsional mode were studied, and it was found that the first torsional mode is dominant after a train has passed, while the first bending mode is dominant during the passing of a train. The damping ratio of the first bending mode was 1.5-1.9%, which is slightly lower than the commonly used value 2%. In addition, with the simplified model using the identified dynamic properties, the resonance phenomenon induced by high-speed trains was investigated empirically.