Fig 1 - uploaded by Saeid R Dindarloo
Content may be subject to copyright.
Source publication
Conventionally, many researchers have used both regression and black box techniques to estimate
the unconfined compressive strength (UCS) of different rocks. The advantage of the regression approach
is that it can be used to render a functional relationship between the predictive rock indices and its UCS.
The advantage of the black box techniques i...
Context in source publication
Context 1
... (1992) developed GP as an extension of the traditional GA. In GP the adaptation procedure is composed of thousands of computer programs with different sizes and structures [27]. The optimization task in GA is to find (near) optimal values for a set of given variables. In GP, however, both the solution's structure (e.g., type of the fitness function for a regression problem) and the optimal values of its associated parameters are derived together. In GP, thousands of solutions (computer programs) are generated and evolved consecutively according to the Darwinian principle of survival. A search for the solution begins with a population of completely random programs (solutions) generated from a predefined set of available functions (e.g., arithmetic functions) and terminals (independent variables). All programs are measured against a fitness function (e.g., root mean square error within a regression problem). Consequently, only the best programs survive and will breed to the next generation. The GP can be represented as a hierarchically structured tree comprised of functions and terminals. A simple representation of a GP tree for function y y Z sinx C is illustrated in Fig. 1. The tree reads from left to right and bottom to top. The fittest solutions (smallest error) will be chosen to generate a population of new offspring programs for the next generation, mimicking the Darwinian principle of survival. Next, several genetic operations, namely, mutation, inversion, transposition, recombination and crossover, will generate new offspring from the previous generation's fittest programs. In the mutation, as the most important genetic operator, the operator selects a random node and replaces it with a new node or subtree. Either the error or fitness function is used to evaluate the new offspring. This process continues until a predefined threshold is reached in terms of the best fit or ...
Similar publications
Sakurai proposed hazard warning levels (HWLs) on the basis of the critical strain concept to evaluate the stability of tunnels. When the measured strain values remain smaller than HWL III, the stability of the tunnels is confirmed. The collapses occurred in a number of tunnels existing in Iran have questioned the accuracy of the Sakurai’s criterion...
Determination of rock engineering properties is important in civil, mining and geotechnical engineering. Uniaxial Compressive Strength (UCS) is one of the most important properties of rocks. Point Load Test (PLT) is practically used in geotechnical engineering to determine rock strength index. Despite that the PLT is fast, economical and simple in...
Citations
... Separately from empirical indicators, statistical analysis is reportedly used for PPV prediction Singh 2011, 2013a;Hudaverdi 2012). In this analysis, several input parameters related to blasting design, rock properties and explosive materials were used for ground vibration estimation (Singh and Singh 2005;Khandelwal and Singh 2009;Hajihassani et al., 2015;Dindarloo 2015). Moreover, the application of the statistical analysis may not be reliable if new available data are not the same as the previous ones (Khandelwal and Singh 2009;Mohamed 2011;Verma and Maheshwar 2014). ...
... Based on obtained blasting parameters from Bakhtiari Dam, Iran, Hasanipanah et al., (2015) utilized and introduced a support vector machine (SVM) model to estimate PPV. Dindarloo (2015) developed an SVM model for estimating 100 PPV values collected from the Golegohar iron ore mine, in Iran. They used 12 model inputs of both controllable and non-controllable parameters in order to predict PPV and found that the developed model is a versatile tool for predicting PPV. ...
An investigation was carried out to evaluate the level of ground vibrations induced in a blasting operation at the Felele Quarry Company located in Lokoja, Kogi State, Nigeria. The equipment that was used in measuring readings was the Vibro monitor for some weeks. A digital camera was used to take snapshots of the blast site during the blasting operations and the effects of the blast on the residential buildings within the village vicinity. The results gotten show that all the readings fall within the allowable limits set by the Federal Environmental Protection Agency (FEPA). Moreover, the photographs of the buildings close by revealed cracks in the building walls. Recommendations were made pertaining to the procedure to improve the present blasting operations
... Their analyses revealed that the chosen non-linear multiple regression and ANFIS models could be employed for the estimation of the UCS of the granitic rocks, and the ANFIS model outperformed the non-linear multiple regression model. Dindarloo and Siami-Irdemoosa 30 reported in their studies about the estimating of 117 UCS representative core specimens of carbonate rocks by gene expression programming (GEP). The GEP performed better than ANNs in predicting the UCS of the carbonate rocks. ...
In engineering practices, it is critical and necessary to either measure or estimate the uniaxial compressive strength (UCS) of the rock. Measuring the UCS of rocks requires comprehensive studies in the field and in the laboratory for the rock block sampling, coring, and testing. These studies are time-consuming, expensive and go through difficult processes. Alternatively, the UCS can either be estimated by empirical relationships or predictive models with various measured mechanical and physical parameters of the rocks. Previous studies used different methods to predict UCS, including least squares regression techniques (MLR), adaptive neuro-fuzzy inference system (ANFIS), Sequential artificial neuron networks (SANN), etc. This study is intended to estimate the UCS of the carbonate rock by using a simple, measured Schmidt Hammer (SHV C ) test on core sample and a unit weight (γ n ) of carbonate rock. Principal components regression (PCR), MLR, SANN, and ANFIS are employed to predict the UCS. We are not aware of any study compared the performances of these methods for the prediction of the UCS values. Based on the root mean square error, mean absolute error and R ² , the Sequential artificial neural network has a slight advantage against the other three models.
... They indicated that the performance evaluation of the AN-FIS model was more precise than others. Gene expression programming (GEP) [19] and Multilayer Perceptron Neural Network (MLPNN) [20] were utilized to estimate UCS. Li and Tan [21] suggested a least squares vector machine for the UCS prediction model. ...
Uniaxial compressive strength (UCS) is a critical parameter in the disaster prevention of engineering projects, requiring a large budget and a long time to estimate in different rocks or the early stage of a project. If predicted accurately, the UCS of rocks significantly affects geotechnical applications. This paper develops a dataset of 734 samples from previous studies on different coun-tries' magmatic, sedimentary, and metamorphic rocks. Within the study context, three main factors, point load index, P-wave velocity, and Schmidt hammer rebound number, are utilized to estimate UCS. Moreover, it applies extreme learning machines (ELM) to map the nonlinear relationship between the UCS and the influential factors. Five metaheuristic algorithms, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), butterfly optimization algorithm (BOA), and sparrow search algorithm (SSA), are used to optimize the bias and weight of ELM and thus enhance its predictability. Indeed, several performance parameters are utilized to verify the proposed models' generalization capability and predictive performance. The minimum, maximum, and average relative errors of ELM achieved by the whale optimization algorithm (WOA-ELM) are smaller than the other models, with values of 0.22%, 72.05%, and 11.48%, respectively. In contrast, the minimum and mean residual error produced by WOA-ELM are less than the other models, with values of 0.02 and 2.64 MPa, respectively. The results show that the UCS values derived from WOA-ELM are superior to those from other models. The performance indices (coefficient of determination (R 2): 0.861, mean squared error (MSE): 17.61, root mean squared error (RMSE): 4.20, and value account for (VAF): 91% obtained using the WOA-ELM model indicates high accuracy and reliability, which means that it has broad application potential for estimating UCS of different rocks.
... Their analyses revealed that the chosen non-linear multiple regression and ANFIS models could be employed for the estimation of the UCS of the granitic rocks, and the ANFIS model outperformed the non-linear multiple regression model. Dindarloo and Siami-Irdemoosa 30 reported in their studies about the estimating of 117 UCS representative core specimens of carbonate rocks by gene expression programming (GEP). The GEP performed better than ANNs in predicting the UCS of the carbonate rocks. ...
In engineering practices, it is critical and necessary to either measure or estimate the uniaxial compressive strength (UCS) of the rock. Measuring the UCS of rocks requires comprehensive studies in the field and in the laboratory for the rock block sampling, coring and testing. These studies are time-consuming, expensive and go through difficult processes. Alternatively, the UCS can either be estimated by empirical relationships or predictive models with various measured mechanical and physical parameters of the rocks. Previous studies used different methods to predict UCS, including least squares regression techniques, adaptive neuro-fuzzy inference system, artificial neuron networks and others. This study is intended to estimate the UCS of the carbonate rock by using a simple, measured Schmidt Hammer (SHV C ) test on core sample and a unit weight (γ n ) of carbonate rock. Principal components regression, multiple regression and artificial neural networks are employed to predict the UCS. We are not aware any study compared the performance of these methods for the prediction of the UCS values. The results of those models are very close; however, based on both roots mean square error (RMSE) and mean absolute error (MAE), artificial neural networks (ANN) have a slight advantage against the other two models.
... For solving regression and classification problems, GP provides several computer programs. The optimal values of some predefined parameters are obtained by GA, while finding both the best models and best parameters for a set of variables produced by GP is based on the Darwinian evolution theory [39]. There are also several other applications of such techniques in the literature that have been investigated insightfully in recent years [40,41]. ...
The significance of spillways is to allow the flood to be safely discharged from downstream. There is a strong correlation between the poor design of spillways and the failures of dams. In order to address this concern, the present study investigates the flow over the Nazloo-ogee spillway using the CFD 3D numerical model and an artificial intelligence method called Gene Expression Programming (GEP). In a physical model, discharge and flow depths were calculated for 21 different total heads. Among different turbulence models, the RNG turbulence model achieved the maximum compatibility in computational fluid dynamic simulation. In addition, GEP was used to estimate Q, in which 70% of collected data was dedicated to training and 30% to testing. R2, RMSE, and MAE were obtained as performance criteria, and the new mathematical equation for the prediction of discharge was obtained using this model. Finally, the numerical model and GEP outputs were compared with the experimental data. According to the results, the numerical model and GEP exhibited a high level of correspondence in simulating flow over an ogee-crested spillway.
... They found the FIS model provides high performance. Dindarloo et al. [8] determined the UCS of carbonate rocks by means of gene expression programming (GEP). Armaghani et al. [2] used the GEP in the determination of the UCS of sandstone rock samples and concluded that GEP is superior when compared to linear multiple regression. ...
... The predicted UCS values are much nearer to the actual values for the PDWLSTSVR model (Figs. 7,8,9,10,11 and 12), whereas, in the other models, the predicted values are dispersed. Thus, the PDWLSTSVR model is able to accurately predict the UCS values in comparison with other models for both small and large datasets. ...
In this study, the uniaxial compressive strength (UCS) of rock samples has been predicted using a novel machine learning (ML) algorithm. The efficacy of the algorithm was evaluated by testing the same on a tiny dataset with only 47 samples as well as a large dataset with 170 samples. The UCS of rock samples has some outlier points in the dataset. It is well known that the samples are equally responsible for the end regressor in the case of random forest (RF), extreme learning machine (ELM), least squares support vector regression (LSSVR) primal least squares twin SVR (PLSTSVR), and even few of them act as outliers. Due to this, the prediction performance may degrade. In this study, a new density weighted approach for PLSTSVR is proposed as density weighted least squares twin support vector regression (PDWLSTSVR) in primal space, to deal with input samples in the presence of outliers. Hence, it boosts the performance of PDWLSTSVR in terms of efficiency. Here, the weights are determined with the help of k–Nearest Neighbour (k–NN) distance. Further, the proposed PDWLSTSVR is applied to real-world application like the prediction of the UCS of rock samples. To assess the competence of the proposed PDWLSTSVR, the performance of the models is tested based on different evaluation measures like RMSE, MAE, SMAPE, MASE, SSE/SST, SSR/SST and R2. The result shows that PDWLSTSVR outperforms the RF, ELM, LSSVR and PLSTSVR in terms of all the evaluation measures.
... Ozbek et al. (2013) estimated UCS value of basalt and four ignimbrite (black, yellow, gray, brown) samples by means of GEP using rock properties like water absorption by weight and unit weight and porosity [52]. Dindarloo and Siami-Irdemoosa (2015) predicted UCS value of carbonate rocks by means of GEP, using two parameters of total porosity and P-wave velocity of rocks [53]. Behnia et al. (2017) predicted UCS of rocks by means of GEP using some engineering properties like quartz content, dry density and porosity [54]. ...
Uniaxial compressive strength (UCS) of rocks is the most commonly used parameter in geo-engineering application. However, this parameter is hard for measurement due to a time consuming and requires expensive equipment. Therefore, obtaining this value indirectly using non-destructive testing methods has been a frequently preferred method for a long time. In order to obtain multiple regression models, input parameters need many assumptions. Thus, the estimation of the mechanical properties of rocks using by machine learning methods has been investigated. In this study, UCS values of rocks were estimated by reformulating with artificial intelligence-based age-layered population structure genetic programming (ALPS-GP) which is one of machine learning methods. Artificial neural network (ANN) and ALPS-GP models were performed to predict UCS from porosity, Schmidt hammer hardness and ultrasonic wave velocity test methods. For this purpose, the mentioned three tests (porosity, Schmidt hammer hardness and P-wave velocity) were carried out on ten different stones from Turkey. ANN was performed to evaluate this new technique. Reliability of UCS values determined by models was checked with mean absolute error (MAE), coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) values. These values were calculated as 1.64, 0.98, 2.11 and 99.81 for ANN, and 2.11, 0.98, 2.50 and 97.86 for ALPS-GP, respectively. It was observed that both methods used were quite successful in UCS estimation. The most important advantage of the ALPS-GP model is providing an equation for UCS estimation. In the light of the obtained findings, it has been revealed that this equation derived from ALPS-GP can be used in UCS estimation processes of similar rock types (limestone, dolomite and onyx).
... In this program, linear and simple constantlength chromosomes are used in the genetic algorithm and branch structures of different sizes and shapes are combined with expression trees in genetic planning [81]. The first step in the model algorithm is to generate the initial population of solutions, which can be done by random sampling or taking into information about the problem [82]. Chromosomes are expressed as expression tree and evaluated by fit function, if the desired solution or the arrival of generations, the evolution is stopped and the best solution is provided [83,84]. ...
The history of tunnel boring machine (TBM) tunnelling dates back to nearly 50 years ago. Due to high construction cost, the investigation on TBM performance is regarded as one of the crucial issues which should be considered from different aspects. The prediction of TBM penetration rate is one of the most important part of every mechanized tunnelling project which plays a key role in selecting the machine as well. One of the major difficulties and challenges in TBM performance prediction is to apply novel approaches to predict the TBMs penetration rate. Considering the importance of this issue, the objective of this research work is to attain more realistic models for predicting TBM penetration rate in Iranian water conveyance tunneling. With this respect, a database comprises field data and machine parameters in Chamshir water conveyance tunneling project were established. The data were then analyzed through artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP). Results demonstrated that obtained values of the coefficient of determination (R²) and the root mean square error (RMSE) found to be 0.99 and 0.15 for ANN, 0.95 and 0.37 for SVM, 0.99 and 0.11 for GEP, respectively. These models can be applied to predict TBM penetration rate in the Chamshir water conveyance tunnel. Moreover, it can be concluded that the GEP method has the higher accuracy (maximum R² and minimum RMSE) among all predictive models.
... It has been increasingly used in many engineering disciplines such as geological, environmental, and civil engineering because of the fact that it has produced very simple and effective mathematical formulas. A lot of researchers have estimated the UCS values of different rocks by using GEP (Baykasoğlu et al. 2008;Çanakcı et al. 2009;Ozbek et al. 2013;Dindarloo and Siami-Irdemoosa 2015;Behnia et al. 2017;Armaghani et al. 2018). Baykasoğlu et al. (2008) estimated strength value of limestone, and Çanakcı et al. (2009) estimated strength value of basalt by means of GEP using different rock properties like water absorption by weight, dry density, saturated density, bulk density, and P-wave velocity. ...
... Ozbek et al. (2013) estimated the UCS values of rocks by using index features (i.e., porosity, water absorption by weight and unit weight) of four ignimbrite and basalt samples as inputs. Additionally, Dindarloo and Siami-Irdemoosa (2015) predicted the UCS values of rocks by means of GEP, using total porosity and Pwave velocity of carbonate rocks. Furthermore, Armaghani et al. (2018) and Behnia et al. (2017) estimated the UCS values of rocks by using GEP based on their some engineering properties. ...
Compressive strength of rocks is an important factor in structural design in rock engineering. Compressive strength can be determined in the laboratory by means of the uniaxial compressive strength (UCS) test, or it can be estimated indirectly by simple experiments such as point load strength (PLT) test and Schmidt hammer rebound test. Although the UCS test method is time-consuming and expensive, it is simple when compared to other methods. Therefore, many studies have been performed to estimate UCS values of rocks. Studies indicated that correlation coefficient of rock groups is low unless they are classified as metamorphic, sedimentary, or volcanic. Pyroclastic rocks are widely used as construction materials because of the fact that they crop out over extensive areas in the world. To estimate the UCS values of pyroclastic rocks in Central and Western Anatolia region, Turkey, multiple linear regression (MLR) analysis and gene expression programming (GEP) were employed and during the analysis, and PLT, ρd, ρs, and n were used as the independent variables. Based on the analysis results, it was detected that the GEP methods gave better results than MLR method. Additionally, the correlation coefficient (R²) values of training and sets of validation of the GEP-I model are 0.8859 and 0.9325, respectively, and this model, thereby, is detected the best of generation individuals for prediction of the UCS.
... An increase in the application of the GEP technique for solving many mining and rock mechanics problems has been observed in the recent years. For example, GEP has been successfully applied for prediction of tunneling-induced settlement [40], TBM and roadheader performance [41,42], rock properties such as uniaxial compressive strength, tensile strength, modulus of elasticity [43][44][45][46], side-effects of blasting operation such as ground vibration and flyrock [47][48][49][50], and rockburst hazard [51]. All researchers have pointed out that GEP has the ability to solve complex problems. ...
The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is trained and tested based on a database collected from the experimental results available in the literature. The proposed
GEP model predicts CAI based on two basic geomechanical properties of rocks, i.e.
rock abrasivity index (RAI) and Brazilian tensile strength (BTS). Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2) are used to measure the model performance. Furthermore, the developed GEP model is compared with linear and non-linear multiple regression and other existing models in the literature. The results obtained show that GEP is a strong technique for the prediction of CAI.
