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RESEARCH PAPER · Computational Materials Science · 2023

Enhancing property prediction and process optimization in building materials through machine learning: A review

Konstantinos Stergiou, Charis Ntakolia, Paris Varytis, Elias P. Koumoulos, Patrik Karlsson, Serafeim Moustakidis

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Enhancing property prediction and process optimization in building materials through machine learning: A reviewOriginalApplying Big Data Analysis and Machine Learning Approaches for Optimal Production Management5Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering6Developing a Mathematical Formula that can Calculate the Density of MoO3-B2O3-Bi2O3 Glass System from Weight Percentages of its Components1Exploitation of the Concept of Vicariance to Predict the Space Group of Lithiated Manganese or Cobalt Oxides0Material Quality Filter Model: Machine Learning Integrated with Expert Experience for Process Optimization6Detecting Cutout Shape and Predicting Its Location in Sandwich Structures Using Free Vibration Analysis and Tuned Machine-Learning Algorithms25An Agricultural Unmanned Ground Vehicle for Localized Spraying of Harmful Plants Using Convolutional Neural Networks0Comparison of Power Performance of Horizontal Axis Wind Turbines with NACA 4412 and NREL S809 Airfoils1Consideration of Pumice Sand-Concrete Interface Friction in Pile Modeling1Fatigue Analysis of Leaf Springs1GIS-Based Evaluation of Disaster and Emergency Assembly Areas with AHP in Local Scale6Increasing the Efficiency of Composite Materials Manufacturing Productivity by Using Artificial Intelligent Methods0Influence of T6 heat Treatment on the Mechanical Properties of AA7075 and AA7020 alloys0Interdisciplinary studies on contemporary research practices in engineering in the 21st century-III1Investigation of the Behavior of Steel-Concrete Composite Structure with Different Design Properties Under Far-Fault and Near Fault Earthquake Records0Investigation of the Effects of Different Application Distances and Cutting Parameters on the Machinability of ST37 Steel for the Minimum Quantity Lubrication Method.0Reflection of Industry 4.0 on Logistics Activities: Logistics 4.01Solar Irradiance Prediction and Methods Used in Prediction 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learning approach28Artificial intelligence analysis of the sterilization effects on the mechanical properties of human cranial bones2Characterization and optimization of mechanical properties in design materials using convolutional neural networks and particle swarm optimization5Recent advanced thermal interfacial materials: A review of conducting mechanisms and parameters of carbon materials1Circular economy strategy for the valorization of fly ash as a substitute for cement in monoliths (resistance and reactivity evaluation)4Properties prediction of composites based on machine learning models: A focus on statistical index approaches51Twofold Machine-Learning and Molecular Dynamics: A Computational Framework6Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives164Applications of Deep Learning for Composites Materials2Unlocking the potential of open-tunnel oxides: DFT-guided design and machine 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