Machine Learning-Aided Design of New and Existing Buildings

Dear Colleagues,

Currently, machine learning methods are increasingly being applied in many fields of civil engineering science. The increasing capabilities of hardware (e.g., parallel processing), computer-aided design software, structural analysis software, and software that ensures the performance of machine learning algorithms enable faster and more efficient design of structures.

The seismic design of a new structure or the seismic retrofitting/rehabilitation of an existing one is not straightforward but rather an iterative process that requires a series of tests (e.g., calculation of dimensions and reinforcement of cross-sections, optimum locations and types of the strengthening structural elements, etc.) in order to select the most optimal solution. It should also be noted that the requested solution regarding the optimum design is subject to a number of constraints, e.g., architectural and economic requirements. Therefore, the design of new structures or the seismic retrofitting of existing ones is, in general, an optimization problem with specific constraints. Solving problems of this nature can be accelerated and become more efficient with the aid of machine learning methods that possess the inherent ability to handle multiparametric problems. Therefore, the full or supporting application of these methods in the framework of the design of new structures or the retrofitting/rehabilitation of existing ones can produce safe and cost-effective solutions.

The present Special Issue aims to invite submissions of original research studies, as well as applied studies, presenting the design or retrofitting/rehabilitation of structures with the full or supporting application of machine learning methods. More specifically, the (overlapping) wide research fields covered by this issue are the following:

• Earthquake engineering;
• Machine learning;
• Building information modeling.

Dr. Konstantinos Morfidis
Dr. Konstantinos Kostinakis
Guest Editors

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