RETRACTED ARTICLE: Probabilistic methods and neural networks in structural engineering
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[1] Kornblith S, Norouzi M, Lee H, Hinton G (2019) Similarity of neural network representations revisited. In Int Conf Mach Learn 3519–3529. PMLR
[2] Molchanov P, Mallya A, Tyree S, Frosio I, Kautz J (2019) Importance estimation for neural network pruning. In Proc IEEE/CVF Comput Soc Conf Comput Vis Pattern Recognit 11264–11272
[3] Kriegeskorte N, Golan T (2019) Neural network models and deep learning. Curr Biol 29(7):R231–R236
[4] Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938
[5] Zavadskas EK, Antucheviciene J, Vilutiene T, Adeli H (2018) Sustainable decision-making in civil engineering, construction and building technology. Sustainability 10(1):14
[6] Gopi S (2009) Basic civil engineering. Pearson Education India
[7] Gorse C, Johnston D, Pritchard M (2012) A dictionary of construction, surveying, and civil engineering. Oxford University Press
[8] Le-Hoai L, Dai Lee Y, Nguyen AT (2013) Estimating time performance for building construction projects in Vietnam. KSCE J Civ Eng 17(1):1–8
[9] Cha HS, Kim CK (2011) Quantitative approach for project performance measurement on building construction in South Korea. KSCE J Civ Eng 15(8):1319–1328
[10] Ye XW, Jin T, Yun CB (2019) A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct Syst 24(5):567–585
[11] Love PE, Edwards DJ, Smith J, Walker DH (2009) Divergence or congruence? A path model of rework for building and civil engineering projects. J Perform Constr Facil 23(6):480–488
[12] Çakiroğlu MA, Süzen AA (2020) Assessment and application of deep learning algorithms in civil engineering. El-Cezeri J Sci Eng 7(2):906–922
[13] Tan K (2018) The framework of combining artificial intelligence and construction 3D printing in civil engineering. In MATEC web of conferences 206:01008. EDP Sciences
[14] Yu Z (2021) Big data technology and deep learning algorithm in civil engineering teaching. In J Phys: Conf Ser 1852(3):032023. IOP Publishing
[15] Al Qurishee M, Wu W, Atolagbe B, Owino J, Fomunung I, Onyango M (2020) Creating a dataset to boost civil engineering deep learning research and application. Engineering 12(3):151–165
[16] Yao G, Wei F, Yang Y, Sun Y (2019) Deep-learning-based bughole detection for concrete surface image. Adv Civil Eng 2019
[17] Chou JS, Thedja JP (2016) Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Autom Constr 68:65–80
[18] Shrestha KK, Shrestha PP (2016) A contingency cost estimation system for road maintenance contracts. Proc Eng 145:128–135
[19] Naik MG, Radhika V (2015) Time and cost analysis for highway road construction project using artificial neural networks. KICEM J Construct Eng Proj Manag 4(3):26–31
[20] Praščević N, Praščević Ž (2014) Application of particle swarms for project time-cost optimization. Građevinar 66(12):1097–1107
[21] Othman AA, Torrance JV, Hamid MA (2006) Factors influencing the construction time of civil engineering projects in Malaysia. Eng Construct Arch Manag
[22] Ng ST, Skitmore RM, Lam KC, Poon AW (2004) Demotivating factors influencing the productivity of civil engineering projects. Int J Project Manage 22(2):139–146
[23] Li S, Xie LL (2007) Progress and trend on near-field problems in civil engineering. Acta SeismologicaSinica 20(1):105–114
[24] Tang L, Shen Q, Skitmore M, Cheng EW (2013) Ranked critical factors in PPP briefings. J Manag Eng 29(2):164–171
[25] Head PR (2001) Construction materials and technology: a look at the future. In Proc Insti Civil Eng-Civil Eng 144(3):113–118. Thomas Telford Ltd