State-of-the-Art Review on Probabilistic Seismic Demand Models of Bridges: Machine-Learning Application

Optimizing the serviceability of highway bridges is a fundamental prerequisite to provide proper infrastructure safety and emergency responses after natural hazards such as an earthquake. In this regard, fragility resilience assessment have emerged important means describing potential seismic risk recovery process under uncertain inputs. Generating assessments requires estimating demand bridge components consisting piers, deck, abutment, bearing, etc. The conventional probabilistic model estimate demands was introduced more than two decades ago. Despite extensive body research ever attempting improve models, univariate most common method used in practice. This work presents comprehensive review evolution models capturing machine-learning-based methodologies their advantage comparison model. study sheds light on understanding existing associated attributes along with limitations. also provides appraisal application generate curves subsequent bridges. Moreover, sound reference, highlights opportunities for future development leading enhancement performance applicability models.