Incorporation of a nanotechnology-based additive in cementitious products for clay stabilisation

The mechanical performances and water retention characteristics of clays,stabilised by partial substitution of cement with by-products and inclusion of a nanotechnology-based additive called RoadCem(RC),are studied in this research.The unconfined compression tests and one-dimensional oedometer swelling were performed after 7 d of curing to understand the influence of addition of 1%of RC material in the stabilised soils with the cement partially replaced by 49%,59%and 69%of ground granulated blast furnace slag(GBBS)or pulverised fuel ash(PFA).The moisture retention capacity of the stabilised clays was also explored using the soil-water retention curve(SWRC)from the measured suctions.Results confirmed an obvious effect of the use of RC with the obtained strength and swell properties of the stabilised clays suitable for road application at 50%replacement of cement.This outcome is associated with the in-depth and penetrating hydration of the cementitious materials by the RC and water which results in the production of needle-like matrix with interlocking filaments e a phenomenon referred to as the‘wrapping’effect.On the other hand,the SWRC used to describe the water holding capacity and corresponding swell mechanism of clays stabilised by a proportion of RC showed a satisfactory response.The moisture retention of the RC-modified clays was initially higher but reduced subsequently as the saturation level increased with decreasing suction.This phenomenon confirmed that clays stabilised by including the RC are water-proof in nature,thus ensuring reduced porosity and suction even at reduced water content.Overall,the stabilised clays with the combination of cement,GGBS and RC showed a better performance compared to those with the PFA included.

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers

Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history.Hence,proper determination of a soil’s ability to expand is very vital for achieving a secure and safe ground for infrastructures.Accordingly,this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines(Bayesian linear regression(BLR)&bayes point machine(BPM)support vector machine(SVM)and deep-support vector machine(D-SVM));(multiple linear regressor(REG),logistic regressor(LR)and artificial neural network(ANN)),tree-based algorithms such as decision forest(RDF)&boosted trees(BDT).Also,and for the first time,meta-heuristic classifiers incorporating the techniques of voting(VE)and stacking(SE)were utilised.Different independent scenarios of explanatory features’combination that influence soil behaviour in swelling were investigated.Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable(the actual swell-strain).REG and BLR performed slightly better than ANN while the meta-heuristic learners(VE and SE)produced the best overall performance(greatest R2 value of 0.94 and RMSE of 0.06%exhibited by VE).CEC,plasticity index and moisture content were the features considered to have the highest level of importance.Kernelized binary classifiers(SVM,D-SVM and BPM)gave better accuracy(average accuracy and recall rate of 0.93 and 0.60)compared to ANN,LR and RDF.Sensitivity-driven diagnostic test indicated that the meta-heuristic models’best performance occurred when ML training was conducted using k-fold validation technique.Finally,it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource.