Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction
Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32485/ http://umpir.ump.edu.my/id/eprint/32485/1/Optimized%20adaptive%20neuro-fuzzy%20inference%20system%20using%20metaheuristic.pdf |
| _version_ | 1848824032208617472 |
|---|---|
| author | Liu, Xinni Hussein, Sadaam Hadee Kamarul Hawari, Ghazali Tung, Tran Minh Yaseen, Zaher Mundher |
| author_facet | Liu, Xinni Hussein, Sadaam Hadee Kamarul Hawari, Ghazali Tung, Tran Minh Yaseen, Zaher Mundher |
| author_sort | Liu, Xinni |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling. |
| first_indexed | 2025-11-15T03:06:35Z |
| format | Article |
| id | ump-32485 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:06:35Z |
| publishDate | 2021 |
| publisher | Hindawi Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-324852021-11-09T07:45:43Z http://umpir.ump.edu.my/id/eprint/32485/ Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction Liu, Xinni Hussein, Sadaam Hadee Kamarul Hawari, Ghazali Tung, Tran Minh Yaseen, Zaher Mundher TK Electrical engineering. Electronics Nuclear engineering Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling. Hindawi Limited 2021-03-12 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32485/1/Optimized%20adaptive%20neuro-fuzzy%20inference%20system%20using%20metaheuristic.pdf Liu, Xinni and Hussein, Sadaam Hadee and Kamarul Hawari, Ghazali and Tung, Tran Minh and Yaseen, Zaher Mundher (2021) Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction. Complexity, 2021 (6666699). pp. 1-15. ISSN 1076-2787. (Published) https://doi.org/10.1155/2021/6666699 https://doi.org/10.1155/2021/6666699 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Liu, Xinni Hussein, Sadaam Hadee Kamarul Hawari, Ghazali Tung, Tran Minh Yaseen, Zaher Mundher Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title | Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title_full | Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title_fullStr | Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title_full_unstemmed | Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title_short | Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction |
| title_sort | optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: application of shield tunnelling ground surface settlement prediction |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/32485/ http://umpir.ump.edu.my/id/eprint/32485/ http://umpir.ump.edu.my/id/eprint/32485/ http://umpir.ump.edu.my/id/eprint/32485/1/Optimized%20adaptive%20neuro-fuzzy%20inference%20system%20using%20metaheuristic.pdf |