An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.

Consolidation of partially saturated ground improved by impervious column inclusion:Governing equations and semi-analytical solutions

This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore water for partially saturated soils are proposed in the context of partially-saturated ground improved by impervious column inclusion.Settlement equation and dissipation equations of excess pore air/water pressures for a partially saturated improved ground are then derived.The semi-analytical solutions for ground settlement and pore pressure dissipation are then obtained through the Laplace transform and validated by the existing solutions for two special cases in the literature and the numerical results obtained from the finite difference method.A series of parametric studies is finally conducted to investigate the influence of some key factors on consolidation of partially saturated ground improved by impervious column inclusion.Based on the parametric study,it can be found that a higher value of the area replacement ratio or modulus of the pile results in a longer dissipation time of excess pore air pressure(PAP),a shorter dissipation time of excess pore water pressure(PWP),and a lower normalized settlement.

Calculation of groundwater head distribution with a close barrier during excavation dewatering in confined aquifer

When pumping is conducted in confined aquifer inside excavation pit(waterproof curtain),the direction of the groundwater seepage outside the excavation changes from horizontal to vertical owing to the existence of the curtain barrier.There is no analytical calculation method for the groundwater head distribution induced by dewatering inside excavation.This paper first analyses the mechanism of the blocking effects from a close barrier in confined aquifer.Then,a simple equation based on analytical solution is proposed to calculate groundwater heads inside and outside of the excavation pit with waterproof curtain(hereafter refer to close barrier)in a confined aquifer.The distribution of groundwater head is derived according to two conditions:(i)pumping with a constant water head,and(ii)pumping with a constant flow rate.The proposed calculation equation is verified by both numerical simulation and experimental results.The comparisons demonstrate that the proposed model can be applied in engineering practice of excavation.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Ensemble unit and Al techniques for prediction of rock strain

The behavior of rock masses is influenced by a variety of forces,with measurement of stress and strain playing the most critical roles in assessing deformation.The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material.Many researchers employ AI technology in order to solve these difficulties.AI algorithms such as gradient boosting machine(GBM),support vector regression(SVR),random forest(RF),and group method of data handling(GMDH)are used to efficiently estimate the strain at every point within a rock sample.Additionally,the ensemble unit(EnU)may be utilized to evaluate rock strain.In this study,3000 experimental data are used for the purpose of prediction.The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU.Ranking analysis,stress-strain curve,Young’s modulus,Poisson’s ratio,actual vs.predicted curve,error matrix and the Akaike’s information criterion(AIC)values are used for comparing models.The GBM model achieved 98.16%and 99.98%prediction accuracy(in terms of values of R2)in the longitudinal and lateral dimensions,respectively,during the testing phase.The GBM model,based on the experimental data,has the potential to be a new option for engineers to use when assessing rock strain.