Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure

The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks.This study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in MS-Excel.Focused on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning requirements.The chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an escalation.The SS approach showed exceptional efficacy in calculating odds of failure that are notably low.Within computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO models.The ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most promising.This study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation infrastructure.These findings contribute to the enhancement of safety and reliability in real-world situations.

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.

Application of soft computing techniques for shallow foundation reliability in geotechnical engineering

This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.

Seismic liquefaction potential assessment by using relevance vector machine

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

Determination of rock depth using artificial intelligence techniques

This article adopts three artificial intelligence techniques, Gaussian Process Regression（GPR）, Least Square Support Vector Machine（LSSVM） and Extreme Learning Machine（ELM）, for prediction of rock depth（d） at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.

Performance assessment of genetic programming(GP)and minimax probability machine regression(MPMR)for prediction of seismic ultrasonic attenuation

The determination of seismic attenuation （s） （dB/cm） is a challenging task in earthquake science. This article employs genetic programming （GP） and minimax probability machine regression （MPMR） for prediction of s. GP is developed based on genetic algo- rithm. MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Porosity （n） （%）, permeability （k） （millidarcy）, grain size （d） （μm）, and clay content （c） （%） have been considered as inputs of GP and MPMR. The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural net- work. This article gives robust models based on GP and MPMR for prediction of s.

Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs

This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs.

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.

Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

In many civil engineering projects,Piled Raft Foundations(PRFs)are usually preferred where the incoming load fromthe superstructures is very high.In geotechnical engineering practice,the settlement of soil layers is a critical issue for the serviceability of the structures.Thus,assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase.In this study,reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model.The new approach is an integration of the artificial neural network(ANN)and a recently developed meta-heuristic algorithm called equilibrium optimizer(EO).The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO(i.e.,ANN-EO)against the conventional approach of calculating the probability of failure of PRF.Experimental results show that the proposed ANN-EO attained the most accurate prediction with R^(2)=0.9914 and RMSE=0.0518 in the testing phase,which are significantly better than those obtained from conventional ANN,multivariate adaptive regression splines,and genetic programming,including the ANNoptimized with particle swarmoptimization developed in this study.Based on the experimental results of different settlement values,the newly constructedANN-EOis very potential to analyze the risk associatedwith civil engineering structures.Also,the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce.

Application of Geostatistical Models for Estimating Spatial Variability of Rock Depth

Rock depth information of a site is a significant factor for geotechnical engineering and earthquake ground response analysis. In this paper, reduced level of rock at Bangalore is arrived from the 652 boreholes in the area covering 220 km2. Geostatistical modeling based on kriging (simple and ordinary) techniques has been applied for estimating reduced level of hard rock in Bangalore. The models are used to compute variance of estimated reduced level of the rock. A new type of cross-validation analysis proves the robustness of the developed models. The comparison between the simple and ordinary kriging model demonstrates that the ordinary kriging model is superior to simple kriging model in predicting reduced level of rock in the subsurface of Bangalore.

利用支持向量机和高斯过程回归测定水库诱发的地震(英文)

水库诱发地震震级(M)的预测是在地震工程中的一项重要任务。本文采用支持向量机(SVM)和高斯过程回归(GPR)模型根据水库的参数预测了水库诱发地震震级(M)。综合参数(E)和最大的水库深度(H)作为支持向量机和高斯过程回归模型的输入参数。我们给出一个方程确定水库诱发地震震级(M)。将本文开发的支持向量机和建立的高斯过程回归方法与人工神经网络(ANN)方法相比。结果表明,本文研发的支持向量机和高斯过程回归方法是预测水库诱发地震震级(M)的有效工具。

基于最小二乘支持向量机算法的小地锚抗拔承载力研究（英文）

目的:基于最小二乘支持向量机算法预测小地锚的抗拔承载力。方法:最小二乘支持向量机算法中的输入参数包括等效地锚直径,地锚埋置深度,平均顶椎阻力,平均椎套摩擦力以及安装工艺。使用现场试验的119组数据中的83组数据进行最小二乘支持向量机回归模型分析,并使用剩余的36组数据测试模型的拟合良好性;同时用敏感度分析研究每个输入参数的作用。结论:通过与人工神经网络模型的对比,发现最小二乘支持向量机的性能表现优异。

Liquefaction prediction using support vector machine model based on cone penetration data

A support vector machine(SVM)model has been developed for the prediction of liquefaction susceptibility as a classification problem,which is an imperative task in earthquake engineering.This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test(CPT)data from the 1999 Chi-Chi,Taiwan earthquake.The SVM,a novel learning machine based on statistical theory,uses structural risk minimization(SRM)induction principle to minimize the error.Using cone resistance(q_(c))and cyclic stress ratio(CSR),model has been developed for prediction of liquefaction using SVM.Further an attempt has been made to simplify the model,requiring only two parameters(q_(c)and maximum horizontal acceleration a_(max)),for prediction of liquefaction.Further,developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model.For Chi-Chi earthquake,the model predicts with accuracy of 100%,and in the case of global data,SVM model predicts with accuracy of 89%.The effect of capacity factor(C)on number of support vector and model accuracy has also been investigated.The study shows that SVM can be used as a practical tool for prediction of liquefaction potential,based on field CPT data.

Determination of effective stress parameter of unsaturated soils:A Gaussian process regression approach

This article examines the capability of Gaussian process regression(GPR)for prediction of effective stress parameter(χ)of unsaturated soil.GPR method proceeds by parameterising a covariance function,and then infers the parameters given the data set.Input variables of GPR are net confining pressure(σ_(3)),saturated volumetric water content(θ_(s)),residual water content(θ_(r)),bubbling pressure(h_(b)),suction(s)and fitting parameter(l).A comparative study has been carried out between the developed GPR and Artificial Neural Network(ANN)models.A sensitivity analysis has been done to determine the effect of each input parameter onχ.The developed GPR gives the variance of predictedχ.The results show that the developed GPR is reliable model for prediction ofχof unsaturated soil.

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.

Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns

This study employs a hybrid approach,integrating finite element method(FEM)simulations with machine learning(ML)techniques to investigate the structural performance of double-skin tubular columns(DSTCs)reinforced with glass fiber-reinforced polymer(GFRP).The investigation involves a comprehensive examination of critical parameters,including aspect ratio,concrete strength,number of GFRP confinement layers,and dimensions of steel tubes used in DSTCs,through comparative analyses and parametric studies.To ensure the credibility of the findings,the results are rigorously validated against experimental data,establishing the precision and trustworthiness of the analysis.The present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.

Prediction of bearing capacity of pile foundation using deep learning approaches

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile foundations.This research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first time.Also,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these models.The performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor diagram.Results from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance accuracy.It is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward direction.The prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network approach.In this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.