SHAKING TABLE DYNAMICS: RESULTS FROM A TEST-ANALYSIS COMPARISON STUDY

This paper presents the results of a comprehensive comparison study between the analytically predicted and experimentally identified dynamics of the shaking table system built recently in the Structural Engineering Laboratory at Rice University in Houston, Texas. The primary objectives of the research presented here are two-fold: (1) to shed light into the dynamic performance of asmall-tc-medium size, uni-axial, servo-hydraulic, displacement-controlled shaking table system, and (2) to validate a linearised dynamic model of the system (in the form of the total shaking table transfer function) developed earlier by the authors from basic principles. The analytical model incorporates the inherent dynamic characteristics of the various components of the shaking table system (i.e. controller, servovalve, actuator, test specimen, and reaction mass) and their dynamic interaction.

The test-analysis correlation study performed over a wide range of operating and payload conditions provides useful information on the sensitivity of the shaking table transfer function to control gain parameters and how it can be used to tune the shaking table controller for optimum performance under various payload conditions. The good test-analysis correlation results obtained validate the analytical shaking table model, show its robustness, and provide keen insight into the underlying coupled dynamics of a shaking table system. In order to achieve this good test-analysis correlation, it was crucial to include a time delay in the analytical model of the shaking table system (innovative feature of the model to account for the time lag in the response of the servovalve-actuator system). The expected significant dynamic interaction between payload and shaking table is also confirmed by this study.     

Developing a Synthetic Index of Land Vulnerability to Drought and Desertification

This paper develops an index of the vulnerability of land to drought and desertification (LVI) for Italy. The index takes into account changes in climate, land use, vegetation cover, soil properties, and population during the period 1990–2000. The LVI was built up through a multivariate approach aimed at assessing the importance of the various indicators included in the synthetic index. Increasing land vulnerability was observed during this time, especially in dry areas of the southern regions. This is interpreted as a consequence of land management practices, agricultural intensification, population pressure, and bio-physical degradation. The LVI can be used in an integrated, decision-support system to evaluate the impact of mitigation policies in rural environments.