Abstract: Proper estimation of foundation settlement is a crucial factor in designing shallow foundations. Recent literature shows the applicability of Artificial Neural Networks (ANNs) in predicting the settlement of shallow foundations. However, conventional ANNs have some drawbacks: getting trapped in local minima and a slow rate of learning. Utilization of an optimization algorithm such as Particle Swarm Optimization (PSO) can greatly improve ANN efficiency. In this study, a PSO-based ANN predictive model of settlement is established. A database comprising 80 footing load tests on cohesionless soils compiled from the literature was used for training the predictive model. For training purposes, footing geometrical properties (length, width, and depth of embedment) as well as soil properties (friction angle, stiffness, and effective stress below footing) were used as inputs to the model while the settlement was set to be the output of the model. Close agreement between the settlements predicte

• Publication Date: 02-Jun-2014
• DOI: 10.15224/978-1-63248-012-5-63
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Abstract: A controller software with user interface was developed in Visual Basic that provides user friendly interfaces to control test phases and to enter input test parameters as well as programming capability to control sophisticated and to run high quality triaxial tests for measuring stress-strength-strain properties of soils in obtaining consistent and reliable results from the performed tests. When designing the controller software to administer the entire triaxial test process, the major variables considered primarily and desired to be controlled during the testing progress were: i) boundary drainage conditions (either drained or undrained); ii) imposed stress path; iii) magnitude of axial deformation; and iv) time rate. The software can consistently control the triaxial system applying either undrained or drained linear stress paths as well as performing isotropically (CI) and anisotropically consolidated (CA) or one-dimensionally (CKo) consolidated tests on soil specimens. Additionall

• Publication Date: 02-Jun-2014
• DOI: 10.15224/978-1-63248-012-5-64
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