Assessment and Localization of Structural Damage in r/c Structures through Intelligent Seismic Signal Processing

In this work, a novel approach in post-earthquake structural damage estimation is investigated. The approach is formulated as a problem of both damage approximation and localization. The inter-story drift ratio and the global damage index of Park/Ang (DI G,PA ) are the estimated damage indicators for each floor of the structure. Artificial neural networks (ANNs), random forests (RFs), support vector machines (SVMs) with linear and radial basis function (RBF) kernels and adaptive neuro-fuzzy inference systems (ANFISs) are tested to predict the seismic damage state of each floor of an 8-storey reinforced concrete (r/c) building subjected to 155 natural and artificially generated seismic accelerograms. The damage potential of the accelerograms is described by three seismic parameters extracted from the response of the structure. The set of seismic accelerograms is defined by combining two outlier detection techniques, isolation forests and Z-score, while the set of seismic parameters is confirmed by minimum redundancy maximum relevance (mRMR) feature selection algorithm. Optimization methods are used to fine-tune the performance of all networks. Results indicate RFs and ANNs among the models with optimal performances, reaching average correct classification rates of up to 96.87% and 91.87% with RFs, and 96.25% and 90.12% with ANNs, for DI G,PA and ISDR, respectively.