Area-Wide estimation of seismic building structural types in rural areas by using decision tree and local knowledge in combination

Abstract

Abstract The seismic building structural type (SBST) represents critical information for accurately assessing seismic risk. Such risk is typically determined as a function of seismic hazards, assets such as buildings and people exposed to hazards, and the vulnerability of those exposed elements (especially buildings because SBSTs dictate the main load-bearing structures of buildings and their seismic vulnerability). China has vast rural areas that generally differ in building upgrade status. SBST distribution information for these areas is often outdated, missing, or nonexistent, thus hindering local governments’ planning for earthquake preparedness and mitigation. This study presents a low-cost method for estimating SBSTs in large rural areas. According to building features (e.g., footprint, roof type) extracted from remote sensing data and building-related local knowledge (BRLK) expressed as building group pattern-SBST correlations, a series of SBST recognition rules is established using the decision tree model and statistical methods. The results from experimental verification show the following. Using only decision tree-based recognition, the estimation of SBSTs reaches an overall accuracy of approximately 70%. After combining decision tree-based recognition with BRLK, the overall estimation accuracy exceeds 90%. The proposed method establishes reliable recognition rules for estimating SBSTs in large rural areas and has a strong potential for popularization and application. In the future, the proposed method can be further investigated according to the BRLK in various areas in terms of applicability and adjustment schemes.