Comparative Assessment of Soil-Structure Interaction Regulations of ASCE 7-16 and ASCE 7-10

This paper evaluates the consequences of practicing soil structure interaction (SSI) regulations of ASCE 7-16 on seismic performance of building structures. The motivation for this research stems from the significant changes in the new SSI provisions of ASCE 7-16 compared to the previous 2010 edition. Generally, ASCE 7 considers SSI as a beneficial effect, and allows designer to reduce the design base shear. However, literature shows that this idea cannot properly capture the SSI effects on nonlinear systems. ASCE 7-16 is the first edition of ASCE 7 that considers the SSI effect on yielding systems. This study investigates the consequences of practicing the new provisions on a wide range of buildings with different dynamic characteristics on different soil types. Ductility demand of the structure forms the performance metric of this study, and the probability that practicing SSI provisions, in lieu of fixed-base provisions, increases the ductility demand of the structure is computed. The analyses are conducted within a probabilistic framework which considers the uncertainties in the ground motion and in the properties of the soil-structure system. It is concluded that, for structures with surface foundation on moderate to soft soils, SSI regulations of both ASCE 7-10 and ASCE 7-16 are fairly likely to result in a similar and larger structural responses than those obtained by practicing the fixed-base design regulations. However, for squat and ordinary stiff structures on soft soil or structures with embedded foundation on moderate to soft soils, the SSI provisions of ASCE 7-16 result in performance levels that are closer to those obtained by practicing the fixed-base regulations. Finally, for structures on very soft soils, the new SSI provisions of ASCE 7-16 are likely to rather conservative designs.Comment: ASCE Structures Congress, Fort Worth, TX, USA, April 19-21 (2018

Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference, Austin, TX, USA, June 10-13. (2018