Abstract
Considering the highly variable nature of soil, reliability analysis of pile foundation is being explored in the modern scientific era. The paper investigates the application of relevance vector machines (RVM), generalized regression neural network (GRNN), genetic programming (GP) and adaptive-network-based fuzzy inference (ANFIS) in reliability analysis of settlement of pile group. The simulation is checked using Monte Carlo simulation (M-C). The performance of models is ascertained using various performance parameters and Taylor diagrams. The normality and homogeneity in performance of the models is tested by carrying out Anderson–Darling (AD) test and Mann–Whitney U (M–W) test, respectively. The paper concludes that the performance of RVM, GP and ANFIS were excellent while that of GRNN was poor.
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References
Casagrande A (1965) Civil engineering database. Role of the calculated risk in earthwork and foundation engineering
Phoon KK, Kulhawy FH (1999) Characterization of geotechnical variability. Can Geotech J. https://doi.org/10.1139/t99-038
Cherubini C, Giasi C (1993) The coefficients of variation of some geotechnical parameters. Balkema, Rotterdam
Hoeg K, Murarka RP (1974) Probabilistic analysis and design of a retaining wall. Probab Anal J Geotech Geoenviron Eng 100:349–366
Sivakumar Babu GL, Srivastava A (2007) Reliability analysis of allowable pressure on shallow foundation using response surface method. Comput Geotech. https://doi.org/10.1016/j.compgeo.2006.11.002
Ching J, Phoon KK (2011) A quantile-based approach for calibrating reliability-based partial factors. Struct Saf 33:275–285. https://doi.org/10.1016/j.strusafe.2011.04.002
Samui P, Sitharam TG (2010) Site characterization model using artificial neural network and kriging. Int J Geomech. https://doi.org/10.1061/(ASCE)1532-3641(2010)10:5(171)
Alipour A, Jafari A, Hossaini SMF (2012) Application of ANNs and MVLRA for estimation of specific charge in small tunnel. Int J Geomech. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000125
Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Meas J Int Meas Confed 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007
Farfani HA, Behnamfar F, Fathollahi A (2015) Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines. Expert Syst Appl 42:8971–8981. https://doi.org/10.1016/j.eswa.2015.07.053
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167. https://doi.org/10.1023/A:1009715923555
Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res. https://doi.org/10.1162/15324430152748236
Kaveh A, Fahimi-Farzam M, Kalateh-Ahani M (2012) Time-history analysis based optimal design of space trusses: the CMA evolution strategy approach using GRNN and WA. Struct Eng Mech 44:379–403. https://doi.org/10.12989/sem.2012.44.3.379
Singh S, Murthy TVR (2013) Neural network-based sensor fault accommodation in flight control system. J Intell Syst 22:317–333. https://doi.org/10.1515/jisys-2013-0032
Basser H, Karami H, Shamshirband S et al (2014) Predicting optimum parameters of a protective spur dike using soft computing methodologies—a comparative study. Comput Fluids 97:168–176. https://doi.org/10.1016/j.compfluid.2014.04.013
Hajela P (1990) Genetic search—an approach to the nonconvex optimization problem. AIAA J 28:1205–1210. https://doi.org/10.2514/3.25195
Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. Biosystems 33(1):69–73
Dodagoudar GR, Venkatachalam G (2000) Reliability analysis of slopes using fuzzy sets theory. Comput Geotech. https://doi.org/10.1016/S0266-352X(00)00009-4
Giasi CI, Masi P, Cherubini C (2003) Probabilistic and fuzzy reliability analysis of a sample slope near Aliano. Eng Geol 67:391–402. https://doi.org/10.1016/S0013-7952(02)00222-3
Godil S, Shamim M, Enam S, Qidwai U (2011) Fuzzy logic: a “simple” solution for complexities in neurosciences. Surg Neurol Int. https://doi.org/10.4103/2152-7806.77177
Yazdi JS, Kalantary F, Yazdi HS (2013) Investigation on the effect of data imbalance on prediction of liquefaction. Int J Geomech 13:463–466. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000217
Teimouri R, Sohrabpoor H (2013) Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process. Front Mech Eng 8:429–442. https://doi.org/10.1007/s11465-013-0277-3
Evans M, Kennedy J (2014) Integration of adaptive neuro fuzzy inference systems and principal component analysis for the control of tertiary scale formation on tinplate at a hot mill. Expert Syst Appl 41:6662–6675. https://doi.org/10.1016/j.eswa.2014.05.020
Cornell CA (1969) Probability-based structural code. Am Concr Inst J 66:974–985. https://doi.org/10.14359/7446
Hasofer A, Lind NC (1974) Exact and invariant second-moment code format. J Eng Mech Div 100:111–121
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw. https://doi.org/10.1109/72.97934
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/21.256541
Brown M, Harris C (1994) Neurofuzzy adaptive modelling and control
Holland JH (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI
Ibrahim NM, Rahim NL, Amat RC et al (2012) Determination of plasticity index and compression index of soil at Perlis. APCBEE Procedia. https://doi.org/10.1016/j.apcbee.2012.11.016
Das BM (2014) Advanced soil mechanics, 4th edn. CRC Press, Boca Raton
Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renew Sustain Energy Rev 52:1869–1880
Stone RJ (1993) Improved statistical procedure for the evaluation of solar radiation estimation models. Sol Energy. https://doi.org/10.1016/0038-092X(93)90124-7
Legates DR, Mccabe GJ (2013) A refined index of model performance: a rejoinder. Int J Climatol. https://doi.org/10.1002/joc.3487
Behar O, Khellaf A, Mohammedi K (2015) Comparison of solar radiation models and their validation under Algerian climate—the case of direct irradiance. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2015.03.067
Clancy RM, Kaitala JE, Zambresky LF (1986) The fleet numerical oceanography center global spectral ocean wave model. Bull Am Meteorol Soc 67:498–512. https://doi.org/10.1175/1520-0477(1986)067%3c0498:tfnocg%3e2.0.co;2
Eray O, Mert C, Kisi O (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res 49:1221–1233. https://doi.org/10.2166/nh.2017.076
Domínguez E, Dawson CW, Ramírez A, Abrahart RJ (2011) The search for orthogonal hydrological modelling metrics: a case study of 20 monitoring stations in Colombia. J Hydroinform 13:429–442. https://doi.org/10.2166/hydro.2010.116
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Ranasinghe RATM, Jaksa MB, Pooya Nejad F, Kuo YL (2019) Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results. J Rock Mech Geotech Eng 11:815–823. https://doi.org/10.1016/j.jrmge.2018.10.007
Viscarra Rossel RA, McGlynn RN, McBratney AB (2006) Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma. https://doi.org/10.1016/j.geoderma.2006.07.004
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Kumar, M., Samui, P., Kumar, D. et al. Reliability analysis of settlement of pile group. Innov. Infrastruct. Solut. 6, 24 (2021). https://doi.org/10.1007/s41062-020-00382-z
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DOI: https://doi.org/10.1007/s41062-020-00382-z