Scopus

RETRACTED ARTICLE: Probabilistic methods and neural networks in structural engineering

Năm XB 2023 Tạp chí / Hội thảo International Journal of Advanced Manufacturing Technology Volume 125 (3-4) DOI / Link https://doi.org/10.1007/s00170-022-09335-5 ↗

Tác giả

Tài liệu tham khảo

[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