Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

Tribological behavior of ZK60Gd alloy reinforced by SiC particles after precipitation hardening

In this research, the effect of precipitation hardening on the tribological behavior of the ZK60Gd/SiC composite was studied. For this purpose, ZK60Gd alloy containing with 5 and 10 wt% SiC were produced with stir casting method. The microstructure characterization of the samples showed the wide distributions of Mg_(7)Zn_(3) and Gd(Mg_(0.5)Zn_(0.5)) precipitates were formed during casting. The results of hardness measurement after precipitation hardening at different temperatures showed that the hardness peck was obtained at 175 ℃. The wear tests with different loads(10, 40, 60, 90, and 120 N) and velocities(0.1, 0.3, 0.6, and 0.9 m/s) were performed on the as-cast and heat treated sample at 125, 175, and 225 for 12 h. Between the different precipitation hardening conditions, the precipitation hardened samples at 175 ℃ had the highest hardness values and least wear rate. The sample containing 10% reinforcement had the least wear rate between the unreinforced alloy and the composites. The results showed that abrasive, adhesive, delamination, MML, and fatigue wear mechanisms were the dominant wear mechanisms for the composite samples. In contrast, the dominant wear mechanism for the unreinforced samples was abrasive, adhesive,delamination, MML, and plastic deformation.

Bearing capacity of thin-walled shallow foundations： an experimental and artificial intelligence-based study

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks （ANNs） in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity （Qu）, friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio （Lw/B）. Apart from Qu, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when LJB is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization （PSO）. The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.