Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches
This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate Concrete (RAC). As the construction industry seeks sustainable solutions, RAC has gained prominence due to its environmentally friendly na...
Gespeichert in:
| Veröffentlicht in: | Advances in Engineering and Intelligence Systems Jg. 4; H. 1; S. 21 - 37 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Bilijipub publisher
01.12.2025
|
| Schlagworte: | |
| ISSN: | 2821-0263 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate Concrete (RAC). As the construction industry seeks sustainable solutions, RAC has gained prominence due to its environmentally friendly nature. However, accurately predicting the f'c of RAC remains a complex challenge, owing to the inherent variability of recycled materials. To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). The performance of the LSSVR model is enhanced through the utilization of these optimizers, resulting in its increased proficiency in predicting the f'c of RAC. Through comprehensive experimentation and rigorous analysis, this research showcases the effectiveness of the proposed hybrid method. The findings illustrate significant improvements in the accuracy and reliability of f'c predictions for RAC compared to traditional methods. Moreover, this investigation provides significant perspectives on the synergy between ML and optimization techniques in civil engineering. The most reliable outcomes in this study were achieved through the hybridization of the LSSVR model with GTO. The resulting LSGT model attained the highest R2 value of 0.989 and the lowest RMSE value of 1.618. Overall, the integration of LSSVR with GTO and DOA emerges as a promising methodology to enhance f'c prediction in RAC. This research enhances the current understanding of RAC and highlights its potential for robust hybrid machine-learning approaches in solving real-world challenges within civil engineering. |
|---|---|
| AbstractList | This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate Concrete (RAC). As the construction industry seeks sustainable solutions, RAC has gained prominence due to its environmentally friendly nature. However, accurately predicting the f'c of RAC remains a complex challenge, owing to the inherent variability of recycled materials. To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). The performance of the LSSVR model is enhanced through the utilization of these optimizers, resulting in its increased proficiency in predicting the f'c of RAC. Through comprehensive experimentation and rigorous analysis, this research showcases the effectiveness of the proposed hybrid method. The findings illustrate significant improvements in the accuracy and reliability of f'c predictions for RAC compared to traditional methods. Moreover, this investigation provides significant perspectives on the synergy between ML and optimization techniques in civil engineering. The most reliable outcomes in this study were achieved through the hybridization of the LSSVR model with GTO. The resulting LSGT model attained the highest R2 value of 0.989 and the lowest RMSE value of 1.618. Overall, the integration of LSSVR with GTO and DOA emerges as a promising methodology to enhance f'c prediction in RAC. This research enhances the current understanding of RAC and highlights its potential for robust hybrid machine-learning approaches in solving real-world challenges within civil engineering. |
| Author | Dylan O’Dwyer Samuel Keown |
| Author_xml | – sequence: 1 fullname: Samuel Keown organization: School of Civil Engineering & Assessment Laboratory, University College Dublin, Dublin, Ireland – sequence: 2 fullname: Dylan O’Dwyer organization: School of Civil Engineering & Assessment Laboratory, University College Dublin, Dublin, Ireland |
| BookMark | eNotT21LwzAYDKLgnPsLkj_QmealzT6OMd1gokz9XJ4kT9vIlpS0E-avt758uuOOu-NuyGWIAQm5y9mccybkPaDv55xxNVdM8SKf51wXF2TCNc8zxgtxTWZ97w1TrORacjkhX-vQQrA-NHQVj13C0f9E-jokDM3Q0peEztvBx0B9oHu0Z3tAR5dNk7CBAcdUsAlHMrQpnpqW7qM59QPdnE3yjj6BbX1AukNI4Wdl2XUpjiL2t-SqhkOPs3-ckveH9dtqk-2eH7er5S5zudBDJg0CamVlbmQhioW0papBQJmXQhuutQXk2mgjJdastGhqXjsLsiwtR8jFlGz_el2Ej6pL_gjpXEXw1a8QU1NBGvz4qzJKGytrYxU4qVy5UIuicNwINq46bcU3Rrlx6A |
| ContentType | Journal Article |
| DBID | DOA |
| DOI | 10.22034/aeis.2025.505261.1286 |
| DatabaseName | DOAJ Directory of Open Access Journals |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2821-0263 |
| EndPage | 37 |
| ExternalDocumentID | oai_doaj_org_article_b58bc4fbc5ad45d795966d2b304c7d8c |
| GroupedDBID | AFWDF ALMA_UNASSIGNED_HOLDINGS ARCSS GROUPED_DOAJ |
| ID | FETCH-LOGICAL-d138t-4beae85c41b463694c75fa3a71738b288cae28b8b44ef07cebf2fdca477c2ea13 |
| IEDL.DBID | DOA |
| IngestDate | Fri Oct 03 12:41:10 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-d138t-4beae85c41b463694c75fa3a71738b288cae28b8b44ef07cebf2fdca477c2ea13 |
| OpenAccessLink | https://doaj.org/article/b58bc4fbc5ad45d795966d2b304c7d8c |
| PageCount | 17 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_b58bc4fbc5ad45d795966d2b304c7d8c |
| PublicationCentury | 2000 |
| PublicationDate | 2025-12-01 |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: 2025-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Advances in Engineering and Intelligence Systems |
| PublicationYear | 2025 |
| Publisher | Bilijipub publisher |
| Publisher_xml | – name: Bilijipub publisher |
| SSID | ssib050728424 ssib056857176 |
| Score | 2.3239684 |
| Snippet | This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate... |
| SourceID | doaj |
| SourceType | Open Website |
| StartPage | 21 |
| SubjectTerms | compressive strength dingo optimization algorithm giant trevally optimizer least square support vector regression recycled aggregate concrete |
| Title | Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches |
| URI | https://doaj.org/article/b58bc4fbc5ad45d795966d2b304c7d8c |
| Volume | 4 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals databaseCode: DOA dateStart: 20220101 customDbUrl: isFulltext: true eissn: 2821-0263 dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: false ssIdentifier: ssib050728424 providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources databaseCode: M~E dateStart: 20220101 customDbUrl: isFulltext: true eissn: 2821-0263 dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: false ssIdentifier: ssib050728424 providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELUQYmBBIEB8ywNr2sSxE2csqFUHWlUIULfIdi5tFxe1aaUy8Nu5SyLRjYUlsiLZiu5s373c3TvGHlOptJWJCdCa1iU5IsigMIhSEgvKocGseQo-XtLxWE-n2WSv1RflhDX0wI3guhbXcrK0TplCqoJaYydJISzCcJcW2tHtG6bZHpjCnYRODl67vzzkKtEKcUvSlAgLEcaya2BBdN1CdaiXWxJ1orqgeo-5vzYxg1N20vqGvNd80xk7AH_Ovvp-TpwYfsbp7NZpq1vgFE32s2rOJysKtZB4-cJz9AJ3OLfgvRkCafpFhrM8eoY4aFvy8Nel3awrPtxRsRYf1dmUwFui1RnvtSzjsL5g74P-2_MwaBsmBEUU6yqQFgxo5WRkiQcsQwmp0sSGIu3aCq2dAaEtKkdCGaYObCnKwhmZpk6AieJLduiXHq4YDxFkZ0aVGfpnMgZr8TRBLI1waFVtlFyzJxJU_tlwYuTEUl2_QN3lre7yv3R38x-L3LJj0mKTYnLHDqvVBu7ZkdtWi_Xqod4W-Bx9938A6NvAsA |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhancing+Compressive+Strength+Prediction+in+Recycled+Aggregate+Concrete+through+Robust+Hybrid+Machine+Learning+Approaches&rft.jtitle=Advances+in+Engineering+and+Intelligence+Systems&rft.au=Samuel+Keown&rft.au=Dylan+O%E2%80%99Dwyer&rft.date=2025-12-01&rft.pub=Bilijipub+publisher&rft.eissn=2821-0263&rft.volume=4&rft.issue=1&rft.spage=21&rft.epage=37&rft_id=info:doi/10.22034%2Faeis.2025.505261.1286&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_b58bc4fbc5ad45d795966d2b304c7d8c |