Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation
Abstract The accurate determination of pile-bearing capacity is crucial in construction projects to ensure the stability and safety of structures built on foundation piles. Nevertheless, the conventional estimation methods used for this purpose tend to be resource-intensive and time-consuming. Machi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
|
| Series: | Discover Civil Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44290-025-00215-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850208558979743744 |
|---|---|
| author | Adil Khan Majid Khan Waseem Akhtar Khan Muhammad Ali Afridi Khawaja Atif Naseem Ayesha Noreen |
| author_facet | Adil Khan Majid Khan Waseem Akhtar Khan Muhammad Ali Afridi Khawaja Atif Naseem Ayesha Noreen |
| author_sort | Adil Khan |
| collection | DOAJ |
| description | Abstract The accurate determination of pile-bearing capacity is crucial in construction projects to ensure the stability and safety of structures built on foundation piles. Nevertheless, the conventional estimation methods used for this purpose tend to be resource-intensive and time-consuming. Machine learning (ML) methods offer a promising alternative to traditional modeling techniques for assessing pile-bearing capacity, providing a more robust and efficient approach to estimating pile-bearing capacity. For this purpose, an advanced ML technique, gene expression programming (GEP), was utilized to predict pile-bearing capacity. GEP is a computational technique that mimics biological gene expression processes to evolve computer programs or models capable of solving complex problems through the iterative generation, selection, and recombination of code segments. A dataset of 472 reinforced concrete piles obtained from literature, was employed for training, and validating the model. The ten most optimal parameters were selected as inputs. To ensure robustness and accurate evaluation, the collected dataset was partitioned into three distinct subsets: the training set (70%), the testing set (15%), and the validation set (15%). In addition to external validation assessment, eight statistical indicators were used to assess the performance and validity of the developed model. The developed GEP model exhibited exceptional performance in estimating pile-bearing capacity, demonstrating a high correlation coefficient value of 0.963 during the training phase and a value of 0.962 during the validation and testing phases. Moreover, a simple empirical formulation has been developed based on GEP to estimate the pile-bearing capacity. The SHapley Additive exPlanation analysis revealed that the pile tip elevation exhibited the highest contribution in estimating the pile-bearing capacity among the various factors considered. In summary, this research presents the application of the GEP approach in forecasting the pile-bearing capacity, offering engineers and practitioners a valuable tool for optimizing foundation design and ensuring the stability and safety of structures. |
| format | Article |
| id | doaj-art-773bfb20bb2d447fbb878d75a8122e1e |
| institution | OA Journals |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-773bfb20bb2d447fbb878d75a8122e1e2025-08-20T02:10:13ZengSpringerDiscover Civil Engineering2948-15462025-03-012113010.1007/s44290-025-00215-xPredicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretationAdil Khan0Majid Khan1Waseem Akhtar Khan2Muhammad Ali Afridi3Khawaja Atif Naseem4Ayesha Noreen5Department of Advanced Civil and Structural Engineering, University of BradfordDepartment of Civil Engineering, Southern Illinois University EdwardsvilleDepartment of Civil Engineering, University of Louisiana at LafayetteDepartment of Civil Engineering, University of Louisiana at LafayetteDepartment of Civil Engineering, University of Louisiana at LafayetteDepartment of Civil Engineering, University of Engineering and TechnologyAbstract The accurate determination of pile-bearing capacity is crucial in construction projects to ensure the stability and safety of structures built on foundation piles. Nevertheless, the conventional estimation methods used for this purpose tend to be resource-intensive and time-consuming. Machine learning (ML) methods offer a promising alternative to traditional modeling techniques for assessing pile-bearing capacity, providing a more robust and efficient approach to estimating pile-bearing capacity. For this purpose, an advanced ML technique, gene expression programming (GEP), was utilized to predict pile-bearing capacity. GEP is a computational technique that mimics biological gene expression processes to evolve computer programs or models capable of solving complex problems through the iterative generation, selection, and recombination of code segments. A dataset of 472 reinforced concrete piles obtained from literature, was employed for training, and validating the model. The ten most optimal parameters were selected as inputs. To ensure robustness and accurate evaluation, the collected dataset was partitioned into three distinct subsets: the training set (70%), the testing set (15%), and the validation set (15%). In addition to external validation assessment, eight statistical indicators were used to assess the performance and validity of the developed model. The developed GEP model exhibited exceptional performance in estimating pile-bearing capacity, demonstrating a high correlation coefficient value of 0.963 during the training phase and a value of 0.962 during the validation and testing phases. Moreover, a simple empirical formulation has been developed based on GEP to estimate the pile-bearing capacity. The SHapley Additive exPlanation analysis revealed that the pile tip elevation exhibited the highest contribution in estimating the pile-bearing capacity among the various factors considered. In summary, this research presents the application of the GEP approach in forecasting the pile-bearing capacity, offering engineers and practitioners a valuable tool for optimizing foundation design and ensuring the stability and safety of structures.https://doi.org/10.1007/s44290-025-00215-xPile bearing capacityFoundation designGene expression programmingMachine learningShapley Additive exPlanation |
| spellingShingle | Adil Khan Majid Khan Waseem Akhtar Khan Muhammad Ali Afridi Khawaja Atif Naseem Ayesha Noreen Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation Discover Civil Engineering Pile bearing capacity Foundation design Gene expression programming Machine learning Shapley Additive exPlanation |
| title | Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation |
| title_full | Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation |
| title_fullStr | Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation |
| title_full_unstemmed | Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation |
| title_short | Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation |
| title_sort | predicting pile bearing capacity using gene expression programming with shapley additive explanation interpretation |
| topic | Pile bearing capacity Foundation design Gene expression programming Machine learning Shapley Additive exPlanation |
| url | https://doi.org/10.1007/s44290-025-00215-x |
| work_keys_str_mv | AT adilkhan predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation AT majidkhan predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation AT waseemakhtarkhan predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation AT muhammadaliafridi predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation AT khawajaatifnaseem predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation AT ayeshanoreen predictingpilebearingcapacityusinggeneexpressionprogrammingwithshapleyadditiveexplanationinterpretation |