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

Bardhan, Abidhan; Kardani, Navid; GuhaRay, Anasua; Burman, Avijit; Samui, Pijush; Zhang, Yanmei

doi:10.1016/j.jrmge.2021.06.015

Cited by 42 publications

(14 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to note that, right after model development, various performance metrics including Adj.R 2 , NS, PI, R 2 , RMSE, RSR, VAF, and WI, were used to evaluate hybrid LSSVMs. Note that these indices are frequently used [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] to evaluate the generalization capabilities of any prediction model from a variety of perspectives, including correlation accuracy, related error, variance, and so on. The expressions of these indices can be given as follows:

where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

View full text Add to dashboard Cite

Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

show abstract

where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The tansigmoid function provided the optimum outcomes. Based on the previous literature [60][61][62][63][64], correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) was used for statistical evaluation of the ANN model.…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

“…The developed ANN and GEP models were evaluated using statistical indices, i.e., the values of R, MAE, and RMSE, provided in Figure 8, in accordance with [60][61][62]64,[67][68][69][70][71][72][73][74]. The R values of 0.999 and 0.994 were observed for training and validation data, respectively, for the ANN model (Figure 8a), whereas, for the GEP model, these values were noticed as 0.981 and 0.985 (Figure 8b).…”

Section: Performance Of the Modelsmentioning

confidence: 99%

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

et al. 2022

View full text Add to dashboard Cite

Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

show abstract

“…Eight different performance indices (Equations (3)–(10)), namely the determination coefficient (R 2 ), the performance index (PI), the variance account factor (VAF), Willmott’s index of agreement (WI), the root mean square error (RMSE), the mean absolute error (MAE), the RMSE observation standard deviation ratio (RSR) and the weighted mean absolute percentage error (WMAPE), were determined to evaluate the performance of the developed models [ 38 , 44 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ]. For a flawless prediction model, the values of these indices should be identical to their ideal values, as shown in Table 2 .…”

Section: Data Processing and Analysismentioning

confidence: 99%

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms

et al. 2022

View full text Add to dashboard Cite

The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were constructed. The performance of the developed SVMs was then evaluated. The accuracy of the constructed hybrid models was found to be on the higher side, with R2 ranges between 0.8870 and 0.9774 in the training phase and between 0.8270 and 0.9294 in the testing phase. Based on the experimental results, the developed SVM–HHO (a hybrid model that uses an SVM and the Harris hawks optimization) was overall the most accurate model, with R2 values of 0.9241 and 0.9241 in the training and testing phases, respectively. Experimental results also demonstrate that the developed hybrid SVM can be used as an alternate tool for estimating the ultimate IBS capacity of FRP concrete in civil engineering projects.

show abstract

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

Cited by 42 publications

References 65 publications

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms

Contact Info

Product

Resources

About