Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented

Flexural Performance of Prefabricated Ultra-High-Strength Textile Reinforced Concrete (UHSTRC): An Experimental and Analytical Investigation

Textile Reinforced Concrete (TRC) is a prefabricated novel lightweight high-performance composite material that can be used as a load-bearing or non-load-bearing component of prefabricated buildings. Making TRC with Ultra-High-Strength Concrete (UHSC) (≥100 MPa) can be considered as a potential improvement method to further enhance its properties. This paper investigated the performance of Ultra-High-Strength Textile Reinforced Concrete (UHSTRC) under flexural loading. A detailed experimental program was conducted to investigate the behavior of UHSC on TRC. In the experimental program, a sudden drop in load was observed when the first crack appeared in the UHSTRC. A detailed analytical program was developed to describe and understand such behavior of UHSTRC found in experiments. The analytical program was found to be in good agreement with the experimental results and it was used to carry out an extensive parametric study covering the effects of the number of textile layers, textile material, textile mesh density, and UHSTRC thickness on the performance of UHSTRC. Using a high number of textile layers in thin UHSTRC was found to be more effective than using high-thickness UHSTRC. The high modulus textile layers effectively increase the performance of UHSTRC