García, J.; Villavicencio, G.; Altimiras, F.; Crawford, B.; Soto, R.; Minatogawa, V.; Franco, M.... (2022). Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions. Automation in Construction. 142:1-22. https://doi.org/10.1016/j.autcon.2022.104532
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/190067
Título:
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Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions
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Autor:
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García, Jose
Villavicencio, Gabriel
Altimiras, Francisco
Crawford, Broderick
Soto, Ricardo
Minatogawa, Vinicius
Franco, Matheus
Martínez-Muñoz, D.
Yepes, V.
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Entidad UPV:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports
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Fecha difusión:
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Resumen:
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[EN] Complex industrial problems coupled with the availability of a more robust computing infrastructure present many challenges and opportunities for machine learning (ML) in the construction industry. This paper reviews ...[+]
[EN] Complex industrial problems coupled with the availability of a more robust computing infrastructure present many challenges and opportunities for machine learning (ML) in the construction industry. This paper reviews the ML techniques applied to the construction industry, mainly to identify areas of application and future projection in this industry. Studies from 2015 to 2022 were analyzed to assess the latest applications of ML techniques in construction. A methodology was proposed that automatically identifies topics through the analysis of abstracts using the Bidirectional Encoder Representations from Transformers technique to select main topics manually subsequently. Relevant categories of machine learning applications in construction were identified and analyzed, including applications in concrete technology, retaining wall design, pavement engineering, tunneling, and construction management. Multiple techniques were discussed, including various supervised, deep, and evolutionary ML algorithms. This review study provides future guidelines to researchers regarding ML applications in construction.
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Palabras clave:
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Machine learning
,
BERT
,
Construction
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Concretes
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Retaining walls
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Tunnels
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Pavements
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Construction management
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Derechos de uso:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Fuente:
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Automation in Construction. (issn:
0926-5805
)
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DOI:
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10.1016/j.autcon.2022.104532
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Editorial:
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Elsevier
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Versión del editor:
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https://doi.org/10.1016/j.autcon.2022.104532
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117056RB-I00/ES/OPTIMIZACION HIBRIDA DEL CICLO DE VIDA DE PUENTES Y ESTRUCTURAS MIXTAS Y MODULARES DE ALTA EFICIENCIA SOCIAL Y MEDIOAMBIENTAL BAJO PRESUPUESTOS RESTRICTIVOS/
info:eu-repo/grantAgreement/CONICYT//1210810/
info:eu-repo/grantAgreement/FONDECYT//11180056//Concurso Iniciación en Investigación/
info:eu-repo/grantAgreement/PUCV//039414%2F2021/
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Agradecimientos:
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The authors gratefully acknowledge the funding received from the following research projects:
Jose Garcia was supported by the Grant CONICYT/FONDECYT/INICIACION/, Chile 11180056.
Jose Garcia and Vinicius Minatogawa was ...[+]
The authors gratefully acknowledge the funding received from the following research projects:
Jose Garcia was supported by the Grant CONICYT/FONDECYT/INICIACION/, Chile 11180056.
Jose Garcia and Vinicius Minatogawa was supported by PROYECTODI INVESTIGACION INNOVADORA INTERDISCIPLINARIA, Chile:039.414/2021.
Victor Yepes was supported by Grant PID2020-117056RB-I00 funded by MCIN/AEI/, Spain 10.13039/501100011033 and by ERDF A way of making Europe''.
Francisco Altimiras was supported by the INF-PUCV Scholarship,Chile.
Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1210810, Chile.
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Tipo:
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Artículo
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