{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T18:41:28Z","timestamp":1778179288824,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,4,28]],"date-time":"2016-04-28T00:00:00Z","timestamp":1461801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m \u00d7 1.5 m \u00d7 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.<\/jats:p>","DOI":"10.3390\/e18050167","type":"journal-article","created":{"date-parts":[[2016,4,28]],"date-time":"2016-04-28T10:25:55Z","timestamp":1461839155000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine"],"prefix":"10.3390","volume":"18","author":[{"given":"Iman","family":"Mazinani","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zubaidah","family":"Ismail","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahaboddin","family":"Shamshirband","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Hashim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Faculty of Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marjan","family":"Mansourvar","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erfan","family":"Zalnezhad","sequence":"additional","affiliation":[{"name":"Department of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,4,28]]},"reference":[{"key":"ref_1","first-page":"1040","article-title":"Experimental investigation on tsunami acting on bridges","volume":"8","author":"Mazinani","year":"2014","journal-title":"Int. J. Civ. Archit. Struct. Constr. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.3390\/s8053037","article-title":"On line disaster response community: People as sensors of high magnitude disasters using internet GIS","volume":"8","author":"Laituri","year":"2008","journal-title":"Sensors"},{"key":"ref_3","unstructured":"Kataoka, S., Kusakabe, T., and Nagaya, K. (2006, January 1\u20132). Wave forces acting on bridge girders struck by tsunami. Proceedings of the 12th Japan Earthquake Engineering Symposium, Tokyo, Japan."},{"key":"ref_4","first-page":"902","article-title":"Experiments of tsunami force acting on bridge models","volume":"29","author":"Iemura","year":"2007","journal-title":"J. Earthq. Eng."},{"key":"ref_5","first-page":"801","article-title":"Hydraulic model experiment to simulate the damage of a bridge deck subjected to tsunamis","volume":"53","author":"Shoji","year":"2006","journal-title":"Annu. J. Coast. Eng."},{"key":"ref_6","unstructured":"Sugimoto, T., and Unjoh, S. (2007, January 5\u20137). Hydraulic model tests on the bridge structures damaged by tsunami and tidal wave. Proceedings of the 23th US-Japan Bridge Engineering Workshop, Tsukuba, Japan."},{"key":"ref_7","unstructured":"Araki, S., Ishino, K., and Deguchi, I. (July, January 30). Stability of girder bridge against tsunami fluid force. Proceedings of the 32th International Conference on Coastal Engineering (ICCE), Shanghai, China."},{"key":"ref_8","unstructured":"Thusyanthan, I., and Martinez, E. (2008, January 6\u201311). Model study of tsunami wave loading on bridges. Proceedings of the Eighteenth International Offshore and Polar Engineering, Vancouver, BC, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.proeng.2011.07.136","article-title":"Evaluation of tsunami fluid force acting on a bridge deck subjected to breaker bores","volume":"14","author":"Shoji","year":"2011","journal-title":"Proced. Eng."},{"key":"ref_10","first-page":"115","article-title":"Influence of bridge deck on tsunami loading on inland bridge piers","volume":"4","author":"Lukkunaprasit","year":"2011","journal-title":"IES J. Part A"},{"key":"ref_11","unstructured":"Lau, T.L. (2009). Tsunami force estimation on inland bridges considering complete pier-deck configurations. [Ph.D. Thesis, Chulalongkorn University]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.coastaleng.2014.02.007","article-title":"Experiments and computations of solitary-wave forces on a coastal-bridge deck. Part ii: Deck with girders","volume":"88","author":"Hayatdavoodi","year":"2014","journal-title":"Coast. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mazinani, I., Ismail, Z., and Hashim, A.M. (2015). An overview of tsunami wave force on coastal bridge and open challenges. J. Earthq. Tsunami, 9.","DOI":"10.1142\/S1793431115500062"},{"key":"ref_14","first-page":"6","article-title":"Trends in interactive knowledge discovery for personalized medicine: Cognitive science meets machine learning","volume":"15","author":"Holzinger","year":"2014","journal-title":"Intell. Inform. Bull"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Blanchard, D., Bloice, M., Holzinger, K., Palade, V., and Rabadan, R. (2014, January 11\u201314). Darwin, lamarck, or baldwin: Applying evolutionary algorithms to machine learning techniques. Proceedings of the 2014 IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland.","DOI":"10.1109\/WI-IAT.2014.132"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Holzinger, A., and Jurisica, I. (2014). Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer-Verlag.","DOI":"10.1007\/978-3-662-43968-5"},{"key":"ref_17","unstructured":"Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.neucom.2013.01.063","article-title":"Bankruptcy prediction using extreme learning machine and financial expertise","volume":"128","author":"Yu","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2014.05.068","article-title":"Online sequential extreme learning machine with kernels for nonstationary time series prediction","volume":"145","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.procs.2013.06.043","article-title":"Mobility prediction in mobile ad hoc networks using extreme learning machines","volume":"19","author":"Ghouti","year":"2013","journal-title":"Proced. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.neucom.2012.12.062","article-title":"Fast prediction of protein\u2013protein interaction sites based on extreme learning machines","volume":"128","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.neucom.2013.03.054","article-title":"Extreme learning machine towards dynamic model hypothesis in fish ethology research","volume":"128","author":"Nian","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.renene.2014.08.075","article-title":"Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search","volume":"74","author":"Wong","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.3390\/s150101804","article-title":"A fast and precise indoor localization algorithm based on an online sequential extreme learning machine","volume":"15","author":"Zou","year":"2015","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12174","DOI":"10.3390\/s140712174","article-title":"A smart high accuracy silicon piezoresistive pressure sensor temperature compensation system","volume":"14","author":"Zhou","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mansourvar, M., Shamshirband, S., Raj, R.G., Gunalan, R., and Mazinani, I. (2015). An automated system for skeletal maturity assessment by extreme learning machines. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138493"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_28","unstructured":"Annema, A., Hoen, K., and Wallinga, H. (1994, January 26\u201328). Precision requirements for single-layer feedforward neural networks. Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Turin, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"818","DOI":"10.3390\/e17020818","article-title":"Distributed extreme learning machine for nonlinear learning over network","volume":"17","author":"Huang","year":"2015","journal-title":"Entropy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","article-title":"Universal approximation using incremental constructive feedforward networks with random hidden nodes","volume":"17","author":"Huang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A fast and accurate online sequential learning algorithm for feedforward networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_32","first-page":"256","article-title":"Application of extreme learning machine method for time series analysis","volume":"2","author":"Singh","year":"2007","journal-title":"Int. J. Intell. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(201)","article-title":"Neural networks for river flow prediction","volume":"8","author":"Karunanithi","year":"1994","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(115)","article-title":"Artificial neural networks in hydrology. I: Preliminary concepts","volume":"5","author":"Govindaraju","year":"2000","journal-title":"J. Hydrol. Eng."},{"key":"ref_35","unstructured":"Govindaraju, R.S., and Rao, A.R. (2010). Artificial Neural Networks in Hydrology, Springer Netherlands."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.oceaneng.2008.04.007","article-title":"Real-time wave forecasting using genetic programming","volume":"35","author":"Gaur","year":"2008","journal-title":"Ocean Eng."},{"key":"ref_37","unstructured":"Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Babovic, V., and Keijzer, M. (2006). Rainfall-runoff modeling based on genetic programming. Encycl. Hydrol. Sci.","DOI":"10.1002\/0470848944.hsa017"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1111\/j.1752-1688.2001.tb00980.x","article-title":"Genetic programming and its application in real-time runoff forecasting","volume":"37","author":"Khu","year":"2001","journal-title":"J. Am. Water Resour. Assoc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/18\/5\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:22:59Z","timestamp":1760210579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/18\/5\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,28]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2016,5]]}},"alternative-id":["e18050167"],"URL":"https:\/\/doi.org\/10.3390\/e18050167","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,28]]}}}