Abstract
Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
Highlights
Artificial neural networks (ANNs) are well suited to model the complex behavior of most geotechnical engineering materials which, by their very nature, exhibit extreme variability
Post-2001 applications of ANNs in geotechnical engineering are briefly examined, and interested readers are referred to Shahin et al [1], where the pre-2001 papers are reviewed in some detail
This is because the knowledge extracted by ANNs is stored in a set of weights that are difficult to interpret properly, and due to the large complexity of the network structure, ANNs fail to give a transparent function that relates the inputs to the corresponding outputs
Summary
Artificial neural networks (ANNs) are well suited to model the complex behavior of most geotechnical engineering materials which, by their very nature, exhibit extreme variability. The behavior of deep (pile) and shallow foundations in soils is complex, uncertain, and not yet entirely understood. This fact has encouraged many researchers to apply the ANN technique to the prediction of the behavior of foundations. Classical constitutive modeling based on elasticity and plasticity theories has limited capability to simulate properly the behavior of geomaterials. This is attributed to reasons associated with the formulation complexity, idealization of material behavior, and excessive empirical parameters [20]. Other applications of ANNs in geotechnical engineering include earth retaining structures [56], dams [57, 58], blasting [59], mining [60], environmental geotechnics [61], rock mechanics [62,63,64,65,66,67], site characterization [68], tunnels and underground openings [69,70,71,72,73,74], slope stability and landslides [71, 75,76,77,78,79], and deep excavation [80]
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