Settlement Behavior of Shallow Foundations in Unsaturated Soils under Rainfall
نویسنده
چکیده
Shallow foundations are often situated on unsaturated zones above the groundwater table. In this study, the influence of rainfall infiltration on the settlement behavior of shallow foundations was investigated using numerical analyses. The numerical solutions were compared with experimental data from in-situ load tests. The relative importance of rainfall intensities and groundwater table positions in inducing the additional settlement of shallow foundations was examined through a series of parametric studies. Two different groundwater table positions contributing to settlements and three assorted rainfall intensities were used in the numerical analyses. Typical soil properties of two main residual soils in Korea were incorporated into the numerical analyses. Special attention is given to the sequential analysis procedure comprised of a flow analysis and deformation analysis. Load-settlement relationships obtained from the numerical methodology in the present study were in good agreement with the field measurements. Results from the parametric studies showed that the rainfall intensity plays a significant role in the settlement behavior of shallow foundations in unsaturated soils. The changes in the settlement during rainfall were also affected by the groundwater table position near the ground surface due to changes in matric suction. In addition, higher bearing capacity in response to rainfall infiltration was observed in the soil with smaller permeability function as compared to larger permeability function.
منابع مشابه
Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils
Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic p...
متن کاملArtificial Neural Network−based Settlement Prediction Formula for Shallow Foundations on Granular Soils
The problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. The geotechnical literature has included many formulae that are based on several theoretical or experimental methods to obtain an accurate, or near-accurate, prediction of such settlement. However, these methods fail to achieve consistent success in relation to accu...
متن کاملBehavioral Interference of Vibrating Machines Foundations Constructed on Sandy Soils (RESEARCH NOTE)
In this study, dynamic bearing capacity of adjacent shallow strip foundations located on sandy soil was examined using a numerical finite difference modeling, FLAC. The behavioral interference of shallow strip foundations under different conditions was investigated. The effect of soil strength parameters, geometric characteristics of shallow foundations and cyclic loads at different distance ra...
متن کاملStochastic Simulation of Settlement Prediction of Shallow Foundations Based on a Deterministic Artificial Neural Network Model
The problem of estimating the settlement of shallow foundations on granular soils is complex and not yet entirely understood. In the past, many empirical and theoretical methods have been developed for predicting the settlement of shallow foundations on granular soils; however, these methods are far from accurate and consistent. In recent times, artificial neural networks (ANNs) have been used ...
متن کاملPrediction of Spread Foundations’ Settlement in Cohesionless Soils Using a Hybrid Particle Swarm Optimization-Based ANN Approach
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 S...
متن کامل