Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

Abstract

A major concern in the design of foundations is to achieve a precise estimation of bearing capacity of the underlying soil or rock mass. The present study proposes a new design equation for the prediction of the bearing capacity of shallow foundations on rock masses utilizing artificial neural network (ANN). The bearing capacity is formulated in terms of rock mass rating, unconfined compressive strength of rock, ratio of joint spacing to foundation width, and angle of internal friction for the rock mass. Further, a conventional calculation procedure is proposed based on the fixed connection weights and bias factors of the best ANN structure. A comprehensive database of rock socket, centrifuge rock socket, plate load, and large-scaled footing load test results is used for the model development. Sensitivity and parametric analyses are conducted and discussed. The results clearly demonstrate the acceptable performance of the derived model for estimating the bearing capacity of shallow foundations. The proposed prediction equation has a notably better performance than the traditional equations.

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

“…In the collected database, the ultimate bearing capacity (q ult ) values are initially obtained or interpreted from load-displacement curves using 43 interpreting method. Different parts of the employed database have been used by various researchers to develop models or to conduct different studies on modeling the bearing capacity of foundations resting on or in rock masses and also described in National Cooperative Highway Research Program (NCHRP) report 651 (2010) 7,18,29,44,45 . The descriptive statistics and the set of collected data incorporated in the LGP model are given in Table 3 and Table 4, respectively.…”

“…These techniques become more attractive because of their capability of information processing, such as non-linearity, high parallelism, robustness, fault and failure tolerance and their ability to generalize. Besides, these techniques have been successfully employed to solve problems in civil engineering field [19][20][21][22][23][24][25][26][27][28][29] .…”

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

“…Therefore, the developed model is anticipated to result in considerable savings in terms of testing time, technician cost, and laboratory equipment. It should be noted that LL, PL, and e0 are influenced by natural water content of partially saturated soils, thus making the developed equation applicable to any saturated find-grained soils [28,40,41]. Mathematically, the developed equation had the following structure.…”

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

“…for numerous engineering calculations with a focus on estimation tasks. These models were also successfully used for bearing capacity analysis [12][13][14]. In this sense, Padmini et al [15] employed three models of neuro-fuzzy, ANN, and fuzzy for predicting the ultimate bearing capacity of shallow foundations (on cohesionless soil).…”