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Architectural Engineering

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2022

Machine learning

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Full-Text Articles in Architecture

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen Sep 2022

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the …


Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma Apr 2022

Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the …