Application of ANN in the Performance Evaluation of Composite Recycled Mortar
To promote the large-scale utilization of construction and industrial solid waste in engineering, this study focuses on developing accurate prediction and optimization methods for the unconfined compressive strength (UCS) of composite recycled mortar. Innovatively incorporating three types of recycl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/15/2752 |
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| author | Shichao Zhao Yaohua Liu Geng Xu Hao Zhang Feng Liu Binglei Wang |
| author_facet | Shichao Zhao Yaohua Liu Geng Xu Hao Zhang Feng Liu Binglei Wang |
| author_sort | Shichao Zhao |
| collection | DOAJ |
| description | To promote the large-scale utilization of construction and industrial solid waste in engineering, this study focuses on developing accurate prediction and optimization methods for the unconfined compressive strength (UCS) of composite recycled mortar. Innovatively incorporating three types of recycled powder (RP)—recycled clay brick powder (RCBS), recycled concrete powder (RCBP), and recycled gypsum powder (RCGP)—we systematically investigated the effects of RP type, replacement rate, and curing period on mortar UCS. The core objective and novelty lie in establishing and comparing three artificial intelligence models for high-precision UCS prediction. Furthermore, leveraging GA-BP’s functional extremum optimization theory, we determined the optimal UCS alongside its corresponding mix proportion and curing scheme, with experimental validation of the solution reliability. Key findings include the following: (1) Increasing total RP content significantly reduces mortar UCS; the maximum UCS is achieved with a 1:1 blend ratio of RCBP:RCGP, while a 20% RCBS replacement rate and extended curing periods markedly enhance strength. (2) Among the prediction models, GA-BP demonstrates superior performance, significantly outperforming BP models with both single and double hidden layer. (3) The functional extremum optimization results exhibit high consistency with experimental validation, showing a relative error below 10%, confirming the method’s effectiveness and engineering applicability. |
| format | Article |
| id | doaj-art-a21d66d2d31f483c85c67922b4e5312d |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-a21d66d2d31f483c85c67922b4e5312d2025-08-20T03:35:58ZengMDPI AGBuildings2075-53092025-08-011515275210.3390/buildings15152752Application of ANN in the Performance Evaluation of Composite Recycled MortarShichao Zhao0Yaohua Liu1Geng Xu2Hao Zhang3Feng Liu4Binglei Wang5Jinan City Construction Group Co., Ltd., Jinan 250014, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaJinan Urban Construction Group Co., Ltd., Jinan 250031, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaJinan Urban Construction Group Co., Ltd., Jinan 250031, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaTo promote the large-scale utilization of construction and industrial solid waste in engineering, this study focuses on developing accurate prediction and optimization methods for the unconfined compressive strength (UCS) of composite recycled mortar. Innovatively incorporating three types of recycled powder (RP)—recycled clay brick powder (RCBS), recycled concrete powder (RCBP), and recycled gypsum powder (RCGP)—we systematically investigated the effects of RP type, replacement rate, and curing period on mortar UCS. The core objective and novelty lie in establishing and comparing three artificial intelligence models for high-precision UCS prediction. Furthermore, leveraging GA-BP’s functional extremum optimization theory, we determined the optimal UCS alongside its corresponding mix proportion and curing scheme, with experimental validation of the solution reliability. Key findings include the following: (1) Increasing total RP content significantly reduces mortar UCS; the maximum UCS is achieved with a 1:1 blend ratio of RCBP:RCGP, while a 20% RCBS replacement rate and extended curing periods markedly enhance strength. (2) Among the prediction models, GA-BP demonstrates superior performance, significantly outperforming BP models with both single and double hidden layer. (3) The functional extremum optimization results exhibit high consistency with experimental validation, showing a relative error below 10%, confirming the method’s effectiveness and engineering applicability.https://www.mdpi.com/2075-5309/15/15/2752recycled mortarGA-optimized ANNmix proportion inverse designconstruction waste valorization |
| spellingShingle | Shichao Zhao Yaohua Liu Geng Xu Hao Zhang Feng Liu Binglei Wang Application of ANN in the Performance Evaluation of Composite Recycled Mortar Buildings recycled mortar GA-optimized ANN mix proportion inverse design construction waste valorization |
| title | Application of ANN in the Performance Evaluation of Composite Recycled Mortar |
| title_full | Application of ANN in the Performance Evaluation of Composite Recycled Mortar |
| title_fullStr | Application of ANN in the Performance Evaluation of Composite Recycled Mortar |
| title_full_unstemmed | Application of ANN in the Performance Evaluation of Composite Recycled Mortar |
| title_short | Application of ANN in the Performance Evaluation of Composite Recycled Mortar |
| title_sort | application of ann in the performance evaluation of composite recycled mortar |
| topic | recycled mortar GA-optimized ANN mix proportion inverse design construction waste valorization |
| url | https://www.mdpi.com/2075-5309/15/15/2752 |
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