MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS: APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT - Softcover

Swaroop, A.H.L ; Saathvik, R. Noga ; Dey, Subhashish

9786208451486: MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS: APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT

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ISBN 10: 6208451485 ISBN 13: 9786208451486

Verlag: LAP LAMBERT Academic Publishing, 2025

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Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed.

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�ber die Autorin bzw. den Autor

Dr. A.H.L. Swaroop and Dr. Subhashish Dey are professsor in Civil Engineering Department at Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India. And Mr. R. Noga Saathvik our M.Tech. student in the Structural Engineering Specilization at Civil Engineering Department at SRGEC, Gudlavalleru, Andhra Pradesh, India.

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Verlag: LAP LAMBERT Academic Publishing
Erscheinungsdatum: 2025
Sprache: Englisch
ISBN 10: 6208451485
ISBN 13: 9786208451486
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Anzahl der Seiten: 88
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Machine Learning for Crack Detection on Concrete Structures by Vss

Swaroop, A.H.L; Saathvik, R. Noga; Dey, Subhashish

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MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS: APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT

Swaroop, A.H.L; Saathvik, R. Noga; Dey, Subhashish

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MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed. 88 pp. Englisch. Bestandsnummer des Verk�ufers 9786208451486

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Machine Learning for Crack Detection on Concrete Structures by Vss (Paperback)

A.H.L. Swaroop

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Paperback. Zustand: new. Paperback. Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verk�ufers 9786208451486

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MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS

A. H. L Swaroop

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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 88 pp. Englisch. Bestandsnummer des Verk�ufers 9786208451486

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MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS : APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT

A. H. L Swaroop

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed. Bestandsnummer des Verk�ufers 9786208451486

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MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS | APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT

A. H. L Swaroop (u. a.)

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Taschenbuch. Zustand: Neu. MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES BY VSS | APPLICATION OF MACHINE LEARNING FOR CRACK DETECTION ON CONCRETE STRUCTURES USING CONVOLUTIONAL NEURAL NETWORK ARCHITECT | A. H. L Swaroop (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208451486 | Verantwortliche Person f�r die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verk�ufers 133589317

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