1 |
Alacali, S.N., Akbas, B. and Doran, B. (2011), "Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation", Appl. Soft Comput., 11(2), 2645-2655.
DOI
|
2 |
Alavi, A.H. and Gandomi, A.H. (2011), "Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing", Comput. Struct., 89(23-24), 2176-2194.
DOI
|
3 |
Arafa, M., Alqedra, M. and Najjar, H.A. (2011), "Neural network models for predicting shear strength of reinforced normal and high-strength concrete deep beams", J. Appl. Sci., 11(2), 266-274.
DOI
|
4 |
Attoh Okine, N.O., Cooger, K. and Mensah, S. (2009), "Multivariate Adaptive Regression (MARS) and Hinged Hyperplanes (HHP) for Doweled Pavement Performance Modeling", Constr. Build. Mater., 23, 3020-3023.
DOI
|
5 |
Caglar, N. (2009), "Neural network based approach for determining the shear strength of circular reinforced concrete columns", Constr. Build. Mater., 23(10), 3225-3232.
DOI
ScienceOn
|
6 |
Chua, C.G. (2001), "Prediction of the behavior of braced excavation systems using Bayesian neural networks", Master Thesis, Nanyang Technological University, Singapore.
|
7 |
Chua, C.G. and Goh, A.T.C. (2003), "A hybrid Bayesian back-propagation neural network approach to multivariate modeling", Int. J. Numer. Anal. Meter., 27, 651-667.
DOI
|
8 |
Chuang, P.H., Goh, A.T.C. and Wu, X. (1998), "Modeling the capacity of pin-ended slender reinforced concrete columns using neural networks", J. Struct. Eng., 124(7), 830-838.
DOI
|
9 |
Friedman, J.H. (1991), "Multivariate adaptive regression splines", Ann. Stat., 19, 1-141.
DOI
|
10 |
Gandomi, A.H., Alavi, A.H., Kazemi, S., Alinia, M.M. (2009). "Behavior appraisal of steel semi-rigid joints using linear genetic programming", J. Constr. Steel Res., 65, 1738-1750.
DOI
|
11 |
Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H., (2013), Metaheuristic Applications in Structures and Infrastructures, Elsevier, Waltham, MA, USA.
|
12 |
Goh, A.T.C. (1995), "Neural networks to predict shear strength of deep beams", ACI Struct. J., 92(1), 28-32.
|
13 |
Goh, A.T.C. and Chua, C.G. (2004), "Nonlinear modeling with confidence estimation using Bayesian neural networks", Elect. J. Struct. Eng., 1, 108-118.
|
14 |
Goh, A.T.C. and Zhang, W.G. (2014), "An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines", Eng. Geol., 170, 1-10.
DOI
|
15 |
Gulec, C.K. (2009), "Performance-based assessment and design of squat reinforced concrete shear walls", Ph.D. Thesis, the State University of New York at Buffalo.
|
16 |
Hastie, T., Tibshirani, R. and Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd Edition, Springer.
|
17 |
Jekabsons, G. (2011), ARESLab: Adaptive Regression Splines toolbox for Matlab / Octave, Available at http://www.cs.rtu.lv/jekabsons/
|
18 |
Jenkins, W.M. (2006), "Neural network weight training by mutation", Comput. Struct., 84(31-32), 2107-2112.
DOI
|
19 |
Lashkari, A. (2012), "Prediction of the shaft resistance of nondisplacement piles in sand", Int. J. Numer. Anal. Meter. 37, 904-931.
|
20 |
Mackay, D.J.C. (1991), "Bayesian methods for adaptive models", Ph.D. Thesis, California Institute of Technology.
|
21 |
Mirzahosseini, M., Aghaeifar, A., Alavi, A., Gandomi, A. and Seyednour, R. (2011), "Permanent deformation analysis of asphalt mixtures using soft computing techniques", Expert Syst. Appl., 38(5), 6081-6100.
DOI
ScienceOn
|
22 |
Neal, R.M. (1992), "Bayesian training of back-propagation networks by the hybrid Monte Carlo method", Technical report CRG-TG-92-1, Department of Computer Science, University of Toronto, Canada.
|
23 |
Oreta, A.W.C. and Kawashima, K. (2003), "Neural network modeling of confined compressive strength and strain of circular concrete columns", J. Struct. Eng., 129(4), 554-561.
DOI
|
24 |
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Learning internal representations by error propagation", Parallel Distributed Processing, Eds. D.E. Rumelhart & J.L. McClelland, MIT Press, Cambridge, MA.
|
25 |
Samui, P. (2011), "Determination of ultimate capacity of driven piles in cohesionless soil: a multivariate adaptive regression spline approach", Int. J. Numer. Anal. Meter., 36, 1434-1439.
|
26 |
Samui, P. and Karup, P. (2011), "Multivariate adaptive regression spline and least square support vector machine for prediction of undrained shear strength of clay", IJAMC, 3(2), 33-42.
|
27 |
Samui, P., Das, S. and Kim, D. (2011), "Uplift capacity of suction caisson in clay using multivariate adaptive regression spline", Ocean Eng., 38, 2123-2127.
DOI
|
28 |
Sanad, A. and Saka, M.P. (2001), "Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks", J. Struct. Eng., 127(7), 818-828.
DOI
|
29 |
Tsai, H.C. (2010), "Hybrid high order neural networks", Appl. Soft Comput., 9, 874-881.
|
30 |
Tsai, H.C. (2011), "Using weighted genetic programming to program squat wall strengths and tune associated formulas", Eng. Appl. Artif. Intel., 24, 526-533.
DOI
|
31 |
Yang, K.H., Ashour, A.F., Song, J.K. and Lee, E.T. (2008), "Neural network modeling of RC deep beam shear strength", Struct. Build., 161(1), 29-39.
DOI
|
32 |
Zarnani, S., El-Emam, M. and Bathurst, R.J. (2011), "Comparison of numerical and analytical solutions for reinforced soil wall shaking table tests", Geomech. Eng., 3(4), 291-321.
DOI
|
33 |
Zhang, W. G. and Goh, A. T. C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95.
DOI
|
34 |
Zhang, W.G. and Goh, A.T.C. (2014), "Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns", Geomech. Eng., 7(4), 431-458.
DOI
|
35 |
Zhang, W.G., Goh, A.T.C., Zhang, Y.M., Chen, Y.M. and Xiao, Y. (2015), "Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines", Eng. Geol., 188, 29-37.
DOI
|