Scouring at Bridge Piers Using Artificial Intelligence Models: Implementation and Prediction evaluates the effectiveness of various Artificial Intelligence (AI) models—such as Gene-Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS)—in predicting local scour depth at bridge piers, emphasizing their physical consistency and interpretability compared to traditional methods. The author highlights the limitations of black-box AI models and aims to improve the empirical understanding of scouring data through advanced statistical analysis. Topics include · Methodologies to estimate scouring at bridge piers, · Effective parameters, · AI models and their setting parameters, and · Practical examples of AI models in scour depth prediction. Mohammad Najafzadeh has compiled this book as an innovative resource for bridge designers and professionals in field investigations and consultant engineering, such as hydraulic and transportation engineers, as well as professors and graduate students who seek to explore new potential for scouring at bridge piers using empirical equations and AI models to generate precise estimations of scour depth.
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Mohammad Najafzadeh is an associate professor at the Department of Water Engineering, Faculty of Civil and Surveying Engineering, at the Graduate University of Advanced Technology. He has more than 15 years of professional experience researching sediment transport in ocean and coastal environments, with a focus on scouring at bridge piers, and surface water and groundwater quality using machine learning, remote sensing, and GIS. He has collaborated on numerous international research projects and was selected as Reviewer of the Year in 2019 for the International Water Association's (IWA) Journal of Water Science and Technology. He currently serves on the editorial board of four journals including ASCE's Journal of Pipeline System Engineering and Practice, Elsevier's Ocean Engineering, Springer's Earth Science Informatics, and Taylor & Francis' Marine Georesources and Geotechnology.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : New. Scouring at Bridge Piers Using Artificial Intelligence Models: Implementation and Prediction evaluates the effectiveness of various Artificial Intelligence (AI) models-such as Gene-Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS)-in predicting local scour depth at bridge piers, emphasizing their physical consistency and interpretability compared to traditional methods. The author highlights the limitations of black-box AI models and aims to improve the empirical understanding of scouring data through advanced statistical analysis. Topics include · Methodologies to estimate scouring at bridge piers, · Effective parameters, · AI models and their setting parameters, and · Practical examples of AI models in scour depth prediction. Mohammad Najafzadeh has compiled this book as an innovative resource for bridge designers and professionals in field investigations and consultant engineering, such as hydraulic and transportation engineers, as well as professors and graduate students who seek to explore new potential for scouring at bridge piers using empirical equations and AI models to generate precise estimations of scour depth. N° de réf. du vendeur LU-9780784416334
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Paperback. Etat : new. Paperback. Scouring at Bridge Piers Using Artificial Intelligence Models: Implementation and Prediction evaluates the effectiveness of various Artificial Intelligence (AI) modelssuch as Gene-Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS)in predicting local scour depth at bridge piers, emphasizing their physical consistency and interpretability compared to traditional methods. The author highlights the limitations of black-box AI models and aims to improve the empirical understanding of scouring data through advanced statistical analysis. Topics include Methodologies to estimate scouring at bridge piers, Effective parameters, AI models and their setting parameters, and Practical examples of AI models in scour depth prediction. Mohammad Najafzadeh has compiled this book as an innovative resource for bridge designers and professionals in field investigations and consultant engineering, such as hydraulic and transportation engineers, as well as professors and graduate students who seek to explore new potential for scouring at bridge piers using empirical equations and AI models to generate precise estimations of scour depth. Advanced AI techniquesincluding Gene-Expression Programming, Evolutionary Polynomial Regression, Model Tree, and Multivariate Adaptive Regression Splineare benchmarked for predicting local scour depth at bridge piers. The work highlights the value of physical consistency and interpretability over traditional black-box methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780784416334
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