The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.