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A platform for research: civil engineering, architecture and urbanism
Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation
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.
Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation
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.
Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation
Discov Civ Eng
Khan, Adil (author) / Khan, Majid (author) / Khan, Waseem Akhtar (author) / Afridi, Muhammad Ali (author) / Naseem, Khawaja Atif (author) / Noreen, Ayesha (author)
2025-03-25
Article (Journal)
Electronic Resource
English
| Springer Verlag | 2025
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Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming
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