Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of Advanced Concrete Technology 2022/06/29, Vol.20(6), pp.404-429
Hauptverfasser: Ahmed, Amira Hamdy Ali, Jin, Wu, Ali, Mosaad Ali Hussein
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 429
container_issue 6
container_start_page 404
container_title Journal of Advanced Concrete Technology
container_volume 20
creator Ahmed, Amira Hamdy Ali
Jin, Wu
Ali, Mosaad Ali Hussein
description Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.
doi_str_mv 10.3151/jact.20.404
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2683245440</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2683245440</sourcerecordid><originalsourceid>FETCH-LOGICAL-c476t-6c1cdb9c1fe691391f005405d77b7f6dde1b5634559011f63c8df132b0a1a7f63</originalsourceid><addsrcrecordid>eNo9kF1LwzAUhosoOKdX_oGAN4p0Jk2arl4Ipfgx2HAMvQ5pelIzajuTTNm_N_tgV-fAed5zOE8UXRM8oiQlD0up_CjBI4bZSTQglGUxzQk93fU8HmPCzqML55YY04xm2SDaFNYbbZSRLZp0HtrWNNApQLO-htYh3Vs0t1Ab5U3XoBmoL9kZFei57VcQwuBQr9EC1Ea1UKOiaSw00gMq-05ZCM3toijvHlFpjd8lF_Br4O8yOtOydXB1qMPo8-X5o3yLp--vk7KYxopl3MdcEVVXuSIaeHglJxrjlOG0zrIq07yugVQppyxNc0yI5lSNa01oUmFJZADoMLrZ713Z_mcNzotlv7ZdOCkSPqYJSxnDgbrfU8r2zlnQYmXNt7QbQbDYuhVbtyLBIrgN9NOeXjovGziyMvgIFo4sPwSOg2DPCujoP7evg8U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2683245440</pqid></control><display><type>article</type><title>Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review</title><source>J-STAGE (Japan Science &amp; Technology Information Aggregator, Electronic) Freely Available Titles - Japanese</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Ahmed, Amira Hamdy Ali ; Jin, Wu ; Ali, Mosaad Ali Hussein</creator><creatorcontrib>Ahmed, Amira Hamdy Ali ; Jin, Wu ; Ali, Mosaad Ali Hussein</creatorcontrib><description>Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.</description><identifier>ISSN: 1346-8014</identifier><identifier>EISSN: 1347-3913</identifier><identifier>DOI: 10.3151/jact.20.404</identifier><language>eng</language><publisher>Tokyo: Japan Concrete Institute</publisher><subject>Artificial intelligence ; Concrete aggregates ; Empirical analysis ; Mechanical properties ; Prediction models ; Recycled materials ; State-of-the-art reviews</subject><ispartof>Journal of Advanced Concrete Technology, 2022/06/29, Vol.20(6), pp.404-429</ispartof><rights>2022 by Japan Concrete Institute</rights><rights>Copyright Japan Science and Technology Agency 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-6c1cdb9c1fe691391f005405d77b7f6dde1b5634559011f63c8df132b0a1a7f63</citedby><cites>FETCH-LOGICAL-c476t-6c1cdb9c1fe691391f005405d77b7f6dde1b5634559011f63c8df132b0a1a7f63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,782,786,1885,27931,27932</link.rule.ids></links><search><creatorcontrib>Ahmed, Amira Hamdy Ali</creatorcontrib><creatorcontrib>Jin, Wu</creatorcontrib><creatorcontrib>Ali, Mosaad Ali Hussein</creatorcontrib><title>Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review</title><title>Journal of Advanced Concrete Technology</title><addtitle>ACT</addtitle><description>Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.</description><subject>Artificial intelligence</subject><subject>Concrete aggregates</subject><subject>Empirical analysis</subject><subject>Mechanical properties</subject><subject>Prediction models</subject><subject>Recycled materials</subject><subject>State-of-the-art reviews</subject><issn>1346-8014</issn><issn>1347-3913</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kF1LwzAUhosoOKdX_oGAN4p0Jk2arl4Ipfgx2HAMvQ5pelIzajuTTNm_N_tgV-fAed5zOE8UXRM8oiQlD0up_CjBI4bZSTQglGUxzQk93fU8HmPCzqML55YY04xm2SDaFNYbbZSRLZp0HtrWNNApQLO-htYh3Vs0t1Ab5U3XoBmoL9kZFei57VcQwuBQr9EC1Ea1UKOiaSw00gMq-05ZCM3toijvHlFpjd8lF_Br4O8yOtOydXB1qMPo8-X5o3yLp--vk7KYxopl3MdcEVVXuSIaeHglJxrjlOG0zrIq07yugVQppyxNc0yI5lSNa01oUmFJZADoMLrZ713Z_mcNzotlv7ZdOCkSPqYJSxnDgbrfU8r2zlnQYmXNt7QbQbDYuhVbtyLBIrgN9NOeXjovGziyMvgIFo4sPwSOg2DPCujoP7evg8U</recordid><startdate>20220629</startdate><enddate>20220629</enddate><creator>Ahmed, Amira Hamdy Ali</creator><creator>Jin, Wu</creator><creator>Ali, Mosaad Ali Hussein</creator><general>Japan Concrete Institute</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QQ</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>KR7</scope></search><sort><creationdate>20220629</creationdate><title>Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review</title><author>Ahmed, Amira Hamdy Ali ; Jin, Wu ; Ali, Mosaad Ali Hussein</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-6c1cdb9c1fe691391f005405d77b7f6dde1b5634559011f63c8df132b0a1a7f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Concrete aggregates</topic><topic>Empirical analysis</topic><topic>Mechanical properties</topic><topic>Prediction models</topic><topic>Recycled materials</topic><topic>State-of-the-art reviews</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ahmed, Amira Hamdy Ali</creatorcontrib><creatorcontrib>Jin, Wu</creatorcontrib><creatorcontrib>Ali, Mosaad Ali Hussein</creatorcontrib><collection>CrossRef</collection><collection>Ceramic Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of Advanced Concrete Technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahmed, Amira Hamdy Ali</au><au>Jin, Wu</au><au>Ali, Mosaad Ali Hussein</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review</atitle><jtitle>Journal of Advanced Concrete Technology</jtitle><addtitle>ACT</addtitle><date>2022-06-29</date><risdate>2022</risdate><volume>20</volume><issue>6</issue><spage>404</spage><epage>429</epage><pages>404-429</pages><issn>1346-8014</issn><eissn>1347-3913</eissn><abstract>Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.</abstract><cop>Tokyo</cop><pub>Japan Concrete Institute</pub><doi>10.3151/jact.20.404</doi><tpages>26</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1346-8014
ispartof Journal of Advanced Concrete Technology, 2022/06/29, Vol.20(6), pp.404-429
issn 1346-8014
1347-3913
language eng
recordid cdi_proquest_journals_2683245440
source J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese; EZB-FREE-00999 freely available EZB journals
subjects Artificial intelligence
Concrete aggregates
Empirical analysis
Mechanical properties
Prediction models
Recycled materials
State-of-the-art reviews
title Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-09T10%3A14%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20Intelligence%20Models%20for%20Predicting%20Mechanical%20Properties%20of%20Recycled%20Aggregate%20Concrete%20(RAC):%20Critical%20Review&rft.jtitle=Journal%20of%20Advanced%20Concrete%20Technology&rft.au=Ahmed,%20Amira%20Hamdy%20Ali&rft.date=2022-06-29&rft.volume=20&rft.issue=6&rft.spage=404&rft.epage=429&rft.pages=404-429&rft.issn=1346-8014&rft.eissn=1347-3913&rft_id=info:doi/10.3151/jact.20.404&rft_dat=%3Cproquest_cross%3E2683245440%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2683245440&rft_id=info:pmid/&rfr_iscdi=true