A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

Abstract

Investigations made to evaluate the site-effect characteristics and to develop a reliable site classification scheme have received paramount importance for the urban areas planning and reliable site-specific seismic hazard assessment. This paper presents a novel non-objective and data-driven approach for preliminary seismic site-specific classification maps using machine learning (ML) based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus, which is among the main areas in Saudi Arabia. Besides, the proposed model aims to overcome the clustering error due to the dependency of the interpreter’s experience. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations. Recording at each station lasted for 20 minute length and a sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Frequency and amplification values were used for subsequent site classification by passing messages between data points. The obtained results illustrate that the microtremor spectral ratio can be a remarkably robust tool in determining site effects. Accordingly, the proposed methodology can assist the decision-makers to set the priorities of managing land uses, estimating the earthquake losses, conducting programs for reducing the vulnerability of existing structures, enforcing building codes, planning for emergency response and long-term recovery, and designing and implementing phases of new constructions.

Keywords

• Machine learning
• seismic site classification
• HVSR
• clustering
• affinity propagation