With the aid of the finite element method, the present paper deals with the problem of structural response variability resulting from the spatial variability of material properties of structures, when they are subjected to static loads of a deterministic nature. The spatial variabilities are modeled as two‐dimensional stochastic fields. The finite element discretization is performed in such a way that the size of each element is sufficiently small. Then, the present paper takes advantage of the Neumann expansion technique in deriving the finite element solution for the response variability within the framework of the Monte Carlo method. The Neumann expansion technique permits more detailed comparison between the perturbation and Monte Carlo solutions for accuracy, convergence, and computational efficiency. The result from such a Monte Carlo method is also compared with that based on the commonly used perturbation method. The comparison shows that the validity of the perturbation method is limited to the c...

A method by which sample fields of a multidimensional non‐Gaussian homogeneous stochastic field can be generated is developed. The method first generates Gaussian sample fields and then maps them into non‐Gaussian sample fields with the aid of an iterative procedure. Numerical examples indicate that the procedure is very efficient and generated sample fields satisfy the target spectral density and probability distribution function accurately. The proposed method has a wide range of applicability to engineering problems involving stochastic fields where the Gaussian assumption is not appropriate.

Synthetic aperture radar (SAR) is remarkable for its capability to record the backscattering coefficient, the physical value of the earth's surface, regardless of weather condition or sun illumination. Therefore, SAR is a powerful tool that can be utilized to develop a universal method to comprehend damaged areas in disasters such as earthquakes, forest fires, and floods. We performed a feasibility study on backscattering characteristics of damaged areas in the 1995 Hyogoken‐Nanbu (Kobe), Japan, earthquake using the pre‐ and post‐event ERS images, revealing that the backscattering coefficient and intensity correlation between the two attained values were significantly lowered in hard‐hit areas. The evaluation, however, was performed without speckle noise reduction. We also investigated the effects of speckle noise reduction and pixel‐window size in evaluating building damage using the difference in the backscattering coefficient and correlation coefficient of the pre‐ and post‐event ERS images. From the analysis, an optimum window size for the damage evaluation was obtained. It was also found that the accuracy of damage detection is not significantly improved for speckle‐reduction filtering of window size larger than 21×21 pixels. We developed an automated method to detect hard‐hit areas based on the discriminant analysis, and compared the detected distribution with a damage survey result.

A strong earthquake struck the city of Bam in southeast Iran on 26 December 2003. The earthquake brought massive destruction to the city and its surrounding rural areas. QuickBird, a high‐resolution satellite, captured a clear image of Bam on 03 January 2004, eight days after the event. The city was also observed by QuickBird on 30 September 2003, about three months before the event. In this paper, using the pre‐event image, the location of individual buildings was registered on GIS and the city blocks surrounded by major roads were assigned. Then, the visual damage interpretation based on the European Macroseismic Scale (EMS‐98) was carried out building by building, comparing the pre‐event and post‐event images. The result of the damage inspection was compared with field survey data, and the accuracy and usefulness of the high‐resolution satellite images in damage detection was demonstrated.