Development and applications of an experimental database for structural engineering education

A great number of research organisations in Japan have been conducting structural steel experiments for many years, particularly seismic tests of steel structures like cyclic-loading and pseudo-dynamic tests, in order to determine their seismic performances. However, the original test data gained by most research organisations are not well stored in an appropriate manner for distribution and possible usage by others. With the rapid development of information networks, structural engineers and researchers are able to exchange various types of test data through the Internet. In this paper, the authors present the development of a distributed collaborative database system for structural steel experiments. The database is made available on the Internet, and the use of Java language enables efficient interactive retrieval. The potential applications of the developed database system for structural engineering education are validated for the retrieval of experimental data and seismic numerical analysis.

Data pre-processing for more effective gene clustering

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

Experimental study of damage evolution in cuboid stirrup-confined concrete

This paper presents an experimental study on cuboid stirrup-confined concrete specimens under uniaxial monotonic loading and cyclic loading. The effects of stirrup volume ratio, stirrup yield strength, and concrete strength on damage evolution of the stirrup-confined concrete were investigated. The experimental results showed that the strength and ductility of concrete are improved by appropriate arrangement of the stirrup confinement. Firstly, with the increase of the stirrup volume ratio, the damage evolution of concrete can be relatively restrained. Secondly, higher stirrup yield strength usually leads to larger confining pressures and slower damage evolution. In contrast, higher concrete strength leads to higher brittleness, which accelerates damage evolution. Based on the experimental data, a plastic strain expression is obtained through curve fitting, and a damage evolution equation for stirrup-confined concrete is proposed by introducing a confinement factor (C). The comparisons results demonstrated that the proposed damage evolution curve can accurately describe the experimental results.

Effect of B4C, TiB2 and ZrSiO4 ceramic particles on mechanical properties of aluminium matrix composites: Experimental investigation and predictive modelling

This paper focuses on the influence of processing temperature and inclusion of micron-sized B4C, TiB2 and ZrSiO4 on the mechanical performance of aluminium matrix composites fabricated through stir casting. The ceramic/aluminium composite could withstand greater external loads, due to interfacial ceramic/aluminium bonding effect on the movement of grain and twin boundaries. Based on experimental results, the tensile strength and hardness of ceramic reinforced composite are significantly increased. The maximum improvement is achieved through adding ZrSiO4 and TiB2, which has led to 52% and 125% increase in tensile strength and hardness, respectively. To predict the effect of incorporating ceramic reinforcements on the mechanical properties of composites, experimental data of mechanical tests are used to create 3 models named Levenberg-Marquardt Algorithm (LMA) neural networks. The results show that the LMA- neural networks models have a high level of accuracy in the prediction of mechanical properties for ceramic reinforced-aluminium matrix composites.