Neural network-based performance prediction of marine UHPC with coarse aggregates
In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regre...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Materials |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2025.1550991/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849765807136964608 |
|---|---|
| author | Yunhao Luan Dongbo Cai Deming Wang Changqing Luo Anni Wang Chao Wang Degao Kong Chaohui Xu Sining Huang |
| author_facet | Yunhao Luan Dongbo Cai Deming Wang Changqing Luo Anni Wang Chao Wang Degao Kong Chaohui Xu Sining Huang |
| author_sort | Yunhao Luan |
| collection | DOAJ |
| description | In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). Seven influencing factors were taken as input, such as coarse aggregate type, coarse aggregate content, steel fiber type, steel fiber content, water-binder ratio, rubber particle sand replacement rate and curing system. Mechanical and long-term performance of UHPC-CA were taken as outputs. The results show that artificial neural network can be applied to predict performance of UHPC-CA with multi-parameter input and multi-index output. In terms of the prediction accuracy of mechanical properties and long-term performance of UHPC-CA, the order is GA-BP > PCA-BP > T-BP > multiple nonlinear regression > multiple linear regression. The GA-BP neural network has the highest goodness of fit for the prediction of mechanical properties and long-term performance of UHPC-CA, which is 93.87%, 37.34%, 5.13% and 3.21% averagely higher than that of multiple linear regression, multiple nonlinear regression, T-BP and PCA-BP, respectively. Furthermore, GA-BP neural network has the lowest error index for each performance prediction. MAE, MSE and RMSE are 18.13%, 77.26% and 52.31% lower than PCA-BP on average. |
| format | Article |
| id | doaj-art-5bb2faa1832b48bd8644ddb20932c70f |
| institution | DOAJ |
| issn | 2296-8016 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj-art-5bb2faa1832b48bd8644ddb20932c70f2025-08-20T03:04:45ZengFrontiers Media S.A.Frontiers in Materials2296-80162025-02-011210.3389/fmats.2025.15509911550991Neural network-based performance prediction of marine UHPC with coarse aggregatesYunhao Luan0Dongbo Cai1Deming Wang2Changqing Luo3Anni Wang4Chao Wang5Degao Kong6Chaohui Xu7Sining Huang8College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, ChinaThe Seventh Engineering Co., Ltd., of CCCC First Highway Engineering Co. Ltd., Zhengzhou, ChinaSchool of Transportation and Civil Engineering, Shandong Jiaotong University, Jinan, ChinaThe Seventh Engineering Co., Ltd., of CCCC First Highway Engineering Co. Ltd., Zhengzhou, ChinaCollege of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, ChinaThe Seventh Engineering Co., Ltd., of CCCC First Highway Engineering Co. Ltd., Zhengzhou, ChinaThe Seventh Engineering Co., Ltd., of CCCC First Highway Engineering Co. Ltd., Zhengzhou, ChinaThe Seventh Engineering Co., Ltd., of CCCC First Highway Engineering Co. Ltd., Zhengzhou, ChinaCollege of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, ChinaIn order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). Seven influencing factors were taken as input, such as coarse aggregate type, coarse aggregate content, steel fiber type, steel fiber content, water-binder ratio, rubber particle sand replacement rate and curing system. Mechanical and long-term performance of UHPC-CA were taken as outputs. The results show that artificial neural network can be applied to predict performance of UHPC-CA with multi-parameter input and multi-index output. In terms of the prediction accuracy of mechanical properties and long-term performance of UHPC-CA, the order is GA-BP > PCA-BP > T-BP > multiple nonlinear regression > multiple linear regression. The GA-BP neural network has the highest goodness of fit for the prediction of mechanical properties and long-term performance of UHPC-CA, which is 93.87%, 37.34%, 5.13% and 3.21% averagely higher than that of multiple linear regression, multiple nonlinear regression, T-BP and PCA-BP, respectively. Furthermore, GA-BP neural network has the lowest error index for each performance prediction. MAE, MSE and RMSE are 18.13%, 77.26% and 52.31% lower than PCA-BP on average.https://www.frontiersin.org/articles/10.3389/fmats.2025.1550991/fullultra-high performance concrete with coarse aggregate (UHPC-CA)neural networkprediction modelmechanical propertieslong-term durability |
| spellingShingle | Yunhao Luan Dongbo Cai Deming Wang Changqing Luo Anni Wang Chao Wang Degao Kong Chaohui Xu Sining Huang Neural network-based performance prediction of marine UHPC with coarse aggregates Frontiers in Materials ultra-high performance concrete with coarse aggregate (UHPC-CA) neural network prediction model mechanical properties long-term durability |
| title | Neural network-based performance prediction of marine UHPC with coarse aggregates |
| title_full | Neural network-based performance prediction of marine UHPC with coarse aggregates |
| title_fullStr | Neural network-based performance prediction of marine UHPC with coarse aggregates |
| title_full_unstemmed | Neural network-based performance prediction of marine UHPC with coarse aggregates |
| title_short | Neural network-based performance prediction of marine UHPC with coarse aggregates |
| title_sort | neural network based performance prediction of marine uhpc with coarse aggregates |
| topic | ultra-high performance concrete with coarse aggregate (UHPC-CA) neural network prediction model mechanical properties long-term durability |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2025.1550991/full |
| work_keys_str_mv | AT yunhaoluan neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT dongbocai neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT demingwang neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT changqingluo neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT anniwang neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT chaowang neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT degaokong neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT chaohuixu neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates AT sininghuang neuralnetworkbasedperformancepredictionofmarineuhpcwithcoarseaggregates |