Ikumasa Yoshida

Settlement prediction by spatial-temporal random process using Asaoka's method

A methodology was presented for observation-based settlement prediction with consideration of the spatial correlation structure of soil. The spatial correlation is introduced among the settlement model parameters and the settlements at various points are spatially correlated through these geotechnical parameters, which naturally describe the phenomenon. The method is based on Bayesian estimation by considering both prior information, including spatial correlation and observed settlement, to search for the best estimates of the parameters at any arbitrary points on the ground. Within the Bayesian framework, the optimised selection of auto-correlation distance by Akaikes Bayesian Information Criterion (ABIC) is also proposed. The application of the proposed approach in consolidation settlement prediction using Asaokas method is presented in this paper. Several case studies were carried out using simulated settlement data to investigate the performance the proposed approach. It is concluded that the accuracy of the settlement prediction can be improved by taking into account the spatial correlation structure and the proposed approach gives the rational prediction of the settlement at any location at any time with quantified uncertainty.

PRIORITIZATION OF SEISMIC REINFORCEMENT FOR ROAD BRIDGES BASED ON THE CONCEPT OF LIFE CYCLE COST

This paper proposes a methodology to support decision making for prioritization seismic reinforcement of existing structures based on the concept of Life Cycle Cost taking into account seismic risk. The paper applies the methodology to actual road bridges in Hanshin Expressway Public Corporation as an example and the applicability of this methodology is examined.

Reliability Analysis of Long River Dike Against Liquefaction Failure

The authors proposed a reliability analysis method for long continuous structures considering investigation sites to counter liquefaction failure. This method is characterized by continuously calculating the failure probability caused by liquefaction at an arbitrary point and quantifying the uncertainty concerning the investigation site. The purpose of this study is to verify its effectiveness based on application to an actual river dike that suffered liquefaction failure due to the 2011 Earthquake off the Pacific coast of Tohoku.

A Group of Deterioration Curves of Bridges Estimated by Extended EM Algorithm

This study proposes a method to separate data into several groups and perform regression analysis for each group at the same time statistically by using EM (Expectation and Maximization) algorithm or its extended formulation to nonlinear regression problem. With using the proposed method, a group of deterioration curves is estimated based on database of health index of 341 bridges by visual inspection in Gifu prefecture, Japan. Ordinary EM algorithm can treat linear regression only, so that the algorithm is extended to nonlinear regression problems. In our proposed method, the hidden parameter and standard deviation of error are estimated following ordinary EM algorithm, whereas nonlinear coefficients are estimated by using Particle Swarm Optimization, which is one of global optimization methods. Deterioration curves of the four bridge groups are estimated with the inspection database by the proposed algorithm.

OPTIMIZATION OF SEISMIC SENSOR LOCATIONS ALONG HIGHWAY LINKS

Optimum positioning of newly installing seismographs along highway lines is discussed. First, we introduce a method to estimate the spatial distribution of earthquake intensity under the condition that several observation data of earthquake intensity are provided at given sensor locations. We also define a spatial index which indicates the estimation error level of the spatial distribution of earthquake intensity of which value becomes zero at the sensor locations. The optimum sensor location problems can be formulated as a minimization problem of this index value which is a function of the sensor locations and the prior information of the special distribution of earthquake intensity in the target area. The design parameters are locations of the sensors. Genetic Algorithm (GA) is used for the optimization. After a brief introduction of real coded GA, optimum sensor locations along highway lines are determined as to minimize the spatial index value by using this GA code.

Probabilistic Approach to Service Life Prediction of Concrete Structures Subjected to Load and Environmental Actions

In the design of reinforced concrete (RC) structures in a marine environment, it is important to consider the effects of this environment on structural long-term performance. In this paper, a time-dependent structural reliability analysis method taking the hazard associated with airborne chlorides into consideration is proposed. Also, a procedure to obtain the failure probabilities of RC structures in a marine environment updated by Sequential Monte Carlo Simulation (SMCS) is indicated. In this procedure, the corrosion crack width is used as observational data. For illustrative purposes, time-dependent reliability analyses are presented for one-way RC slabs in a marine environment. Using SMCS, multiple random variables related to observation information can be updated simultaneously. This is realized by taking into consideration the joint probability density functions of the random variables. The effects of the hazard associated with airborne chlorides and an inspection result of corrosion cracking on the updated estimate of one-way RC slab reliability are discussed in this study.