{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:50:52Z","timestamp":1777495852042,"version":"3.51.4"},"reference-count":74,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":70,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>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\u2010inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro\u2010fuzzy 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\u2010preprocessed and normalized (NPN), and (iv) non\u2010preprocessed 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\u2010PSO model achieved the best predictability performance. In quantitative terms, PPN\u2010ANFIS\u2010PSO 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.<\/jats:p>","DOI":"10.1155\/2021\/6666699","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T23:50:05Z","timestamp":1615593005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Optimized Adaptive Neuro\u2010Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction"],"prefix":"10.1155","volume":"2021","author":[{"given":"Xinni","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadaam Hadee","family":"Hussein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamarul Hawari","family":"Ghazali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tran Minh","family":"Tung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3647-7137","authenticated-orcid":false,"given":"Zaher Mundher","family":"Yaseen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2012.07.004"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1365-1609(02)00112-0"},{"key":"e_1_2_8_3_2","unstructured":"BarlaG.andPelizzaS. 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