Studying the piled raft behavior has been the subject of many types of research in the field of geotechnical engineering. Several studies have been conducted to understand the behavior of these types of foundations, which are often used for uniform loading on the raft and piles with the same length, while generally the transition load from the upper structure to the foundation is non-uniform and the choice of uniform length for piles in the above model will not be optimally economic and practical. The most common method in identifying the behavior of piled rafts is the use of theoretical relationships and software analyses. More precise identification of this type of foundation behavior can be very difficult due to several influential parameters and interaction of set behavior, and it will be done by doing time-consuming computer analyses or costly full-scale physical modeling. In the meantime, the technique of artificial neural networks can be used to achieve this goal with minimum time consumption, in which data from physical and numerical modeling can be used for network learning. One of the advantages of this method is the speed and simplicity of using it. In this paper, a model is presented based on multi-layer perceptron artificial neural network. In this model pile diameter, pile length, and pile spacing is considered as an input parameter that can be used to estimate maximum settlement, maximum differential settlement, and maximum raft moment. By this model, we can create an extensive domain of results for optimum system selection in the desired piled raft foundation. Results of neural network indicate its proper ability in identifying the piled raft behavior. The presented procedure provides an interesting solution and economically enhancing the design of the piled raft foundation system. This innovative design method reduces the time spent on software analyses.
A Goh. Nonlinear modelling in geotechnical engineering using neural networks. Australian Civil Engineering Transactions, 1994, 36(4): 293–297
3
I M Lee, J H Lee. Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 1996, 18(3): 189–200 https://doi.org/10.1016/0266-352X(95)00027-8
4
M S Rahman, J Wang, W Deng, J P Carter. A neural network model for the uplift capacity of suction caissons. Computers and Geotechnics, 2001, 28(4): 269–287 https://doi.org/10.1016/S0266-352X(00)00033-1
5
M A Shahin, M B Jaksa, H R Maier. Predicting the Settlement of Shallow Foundations on Cohesion Less Soils Using Back-Propagation Neural Networks. Research Report No. R167. 2000
6
M A Shahin, M B Jaksa, H R Maier. Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advances in Artificial Neural Systems, 2009, 2009(1): 1–9 https://doi.org/10.1155/2009/308239
7
N Sivakugan, J D Eckersley, H Li. Settlement predictions using neural networks. Australian Civil Engineering Transactions, 1998, CE40: 49–52
M A Shahin. Intelligent computing for modelling axial capacity of pile foundations. Canadian Geotechnical Journal, 2010, 47(2): 230–243 https://doi.org/10.1139/T09-094
10
A Ismail, D S Jeng, L L Zhang. An optimised product-unit neural network with a novel PSO-BP hybrid training algorithm: Applications to load-deformation analysis of axially loaded piles. Engineering Applications of Artificial Intelligence, 2013, 26(10): 2305–2314 https://doi.org/10.1016/j.engappai.2013.04.007
11
Y L Kuo, M B Jaksa, A V Lyamin, W Kaggwa. ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Computers and Geotechnics, 2009, 36(3): 503–516 https://doi.org/10.1016/j.compgeo.2008.07.002
12
M El Gendy. Formulation of a composed coefficient technique for analyzing large piled raft. Ain Shams University, 2007, 42(1): 29–56
13
M Rabiei. Parametric study for piled raft foundations. Electronical Journal of Geotechnical Engineering, 2009, 14(A): 1–9
14
M Rabiei. Effect of pile configuration and loading type on piled raft foundations performance. Deep Foundations and Geotechnical in situ Testing (GSP 205), ASCE, 2010: 34–41
15
M Rabiei, A J Choobbasti. Piled raft design strategies for high rise buildings. Geotechnical and Geological Engineering, 2016, 34(1): 75–85 https://doi.org/10.1007/s10706-015-9929-x
16
M F Badawy, M A Msekh, K M Hamdia, M K Steiner, T Lahmer, T Rabczuk. Hybrid nonlinear surrogate models for fracture behavior of polymeric nanocomposites. Probabilistic Engineering Mechanics, 2017, 50(1): 64–75 https://doi.org/10.1016/j.probengmech.2017.10.003
17
K Fardad, B Najafi, S Faizollahzadeh Ardabili, A Mosavi, S H Shamshirband, T Rabczuk. Biodegradation of medicinal plants waste in an anaerobic digestion reactor for biogas production. Materials and Continua, 2018, 55(3): 381–392
18
Ph Picton. Introduction to Neural Networks. Mc Graw Hill Publication, 1994
M Rabiei, A Janalizadeh Choobbasti. Economic design optimization of piled raft foundations. Innovative Infrastructure Solutions, 2018, 3(1): 65–75 https://doi.org/10.1007/s41062-018-0170-3
21
G Russo, C Viggiani. Factors controlling soil-structure interaction for piled rafts. Darmstadt Geotechnics (Darmstadt University of Technology), 1998, 4(1): 297–322