Dear Colleagues,

Today, studies making approximations without examining various assumptions are insufficient. For this reason, it has become necessary to consider all effects and possibilities in engineering problems, but it may not be possible to model and solve these elements mathematically. This is true for many problems in civil engineering. As an engineering branch in which assumptions are frequently used in mathematical calculations, innovative methods are needed in order to provide sustainable buildings and environments. Artificial intelligence methods come to the fore in this regard.

The multiplicity of possibilities available in these problems can cause innovative methods to take too long. With machine learning, these long processes can be shortened, and user-oriented design and prediction models can be created. In that case, a sustainable built environment will be provided by proposing realistic predicted results and forecasting of unknown issues.  

This Special Issue covers the artificial intelligence methods below, but other relevant methods not listed are also welcome:

• Support vector machine (SVM);
• Individual and ensemble machine learning approaches;
• Deep learning;
• Signal processing methods;
• Artificial neural network (ANN);
• Convolutional neural network (CNN);
• Rapid visual screening (RVS) techniques;
• Metaheuristic algorithms;
• Regression, classification, clustering;
• Dimensionality reduction;
• Transfer learning;
• Reinforcement learning.

The Issue will publish papers which aim to solve and propose new methods in the application of topics including but not limited to the following:

• Progressive damage of reinforced concrete;
• Damage score estimation of existing buildings;
• Prediction of the penetration depth of gravity corers;
• Predicting the splitting tensile strength of recycled aggregate concrete;
• Structural safety assessment;
• Structural health monitoring (SHM);
• Underground utility network characterization;
• Predicting of FRP–concrete interfacial bonding;
• Modelling of shape-memory alloy members;
• Detection of concrete cracks;
• Forecasting the capacity and strength of structural members;
• Fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC);
• Structural control;
• Earthquake-resistant building design;
• Structural optimization;
• Non-destructive structure testing.

Prof. Dr. Gebrail Bekdaş
Dr. Sinan Melih Nigdeli
Prof. Dr. Zong Woo Geem
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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• artificial intelligence
• machine learning
• civil engineering