A Review on Application of Soft Computing Techniques for the Rapid Visual Safety Evaluation and Damage Classification of Existing Buildings

Abstract Seismic vulnerability assessment of existing buildings is of great concern around the world. Different countries develop various approaches and methodologies to overcome the disastrous effects of earthquakes on the structural parameters of the building and the human losses. There are structures still in service with a high seismic vulnerability, which proposes an urgent need for a screening system’s damageability grading system. Rapid urbanization and the proliferation of slums give rise to improper construction practices that make the building stock’s reliability ambiguous, including old structures that were constructed either when the seismic codes were not advanced or not enforced by law. Despite having a good knowledge of structural analysis, it is impractical to conduct detailed nonlinear analysis on each building in the target area to define their seismic vulnerability. This indicates the necessity of developing a rapid, reliable, and computationally easy method of seismic vulnerability assessment, more commonly known as Rapid Visual Screening (RVS). This method begins with a walk-down survey by a trained evaluator, and an initial score is assigned to the structure. Further, the vulnerability parameters are defined (predictor variables), and the damage grades are defined. Various methods are then adopted to develop an optimum correlation between the parameters and damage grades. Soft computing techniques including probabilistic approaches, meta-heuristics, and Artificial Intelligence (AI) theories such as artificial neural networks, machine learning, fuzzy logic, etc. due to their capabilities in targeting inherent imprecision of phenomena in real-world are among the most important and widely used approaches in this regard.