Conventional seismic performance evaluation methods for building structures with soil–structure interaction effects are inefficient for regional seismic damage assessment as a predisaster management system. Therefore, this study presented the framework to develop an artificial neural network-based model, which can rapidly predict seismic responses with soil–structure interaction effects and determine the seismic performance levels. To train, validate and test the model, 11 input parameters were selected as main parameters, and the seismic responses with the soil–structure interaction were generated using a multistep analysis process proposed in this study. The artificial neural network model generated reliable seismic responses with the soil–structure interaction effects, and it rapidly extended the seismic response database using a simple structure and soil information. This data generation method with high accuracy and speed can be utilized as a regional seismic assessment tool for safe and sustainable structures against natural disasters.

Microbially induced calcite precipitation is an emerging environmentally friendly ground improvement technique for a range of geotechnical applications. One of the remaining issues for field implementation of this technique is poor uniformity of calcite, with concentrated precipitation near the injection point, particularly for treatments with continuous injection or in fine sand. Therefore, the work described in this paper performed an experimental investigation to test the hypothesis that using kaolinite particles can increase the number of calcite nucleation sites throughout the sand medium to increase the mass of calcite deposited and provide better distribution of calcite. Both batch and column tests were performed to quantify the impact of kaolinite particles on calcite precipitation. The results indicated that the kaolinite particles may play a role as nucleation sites and facilitate the heterogeneous nucleation of calcite, with observed deposition profiles of kaolinite and breakthrough curves of both kaolinite and bacteria demonstrating that the uniform distribution of deposited kaolinite particles at relatively low inlet concentration (100 mg/l) most effectively increased the amount of calcite, producing a uniform calcite deposition profile. In addition, the results also showed that the well-predicted deposition profile of kaolinite correlated well with the deposited calcite profile.