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• A laboratory database was compiled to assess the performance of gang saw machines. • Two predictive models using GEP and MLR are proposed for gang saw machines. • The GEP-based model was identified as the best model for performance prediction. • The Mohs hardness showed the highest influence on sawing performance. The process of cutting dimension stones by gang saw machines plays a vital role in the productivity and efficiency of quarries and stone cutting factories. The maximum electrical current (MEC) is a key variable for assessing this process. This paper proposes two new models based on multiple linear regression (MLP) and a robust non-linear algorithm of gene expression programming (GEP) to predict MEC. To do so, the parameters of Mohs hardness (Mh), uniaxial compressive strength (UCS), Schimazek's F-abrasiveness factor (SF-a), Young's modulus (YM) and production rate (Pr) were measured as input parameters using laboratory tests. A statistical comparison was made between the developed models and a previous study. The GEP-based model was found to be a reliable and robust modelling approach for predicting MEC. Finally, according to the conducted parametric analysis, Mh was identified as the most influential parameter on MEC prediction. [ABSTRACT FROM AUTHOR]

Accurately predicting key reference points on the axial stress–strain curve of fiber-reinforced polymer (FRP)-confined concrete is of great importance for the pre-design and modeling of structures manufactured with this composite system. This paper presents a detailed study on the development of accurate and practical expressions for predicting the ultimate condition and transition point, as key reference points, on axial stress–strain curves of FRP-confined concrete using generic programming (GP). A comprehensive data tuning and cross-validation analysis was firstly performed to develop prediction models. Afterwards, the accuracy and performance of the developed empirical expressions were examined by sensitivity analysis, parametric analysis and model validation. Finally, a comparison was made between the performance of these proposed expressions and that of the existing best-performing expressions in the literature using statistical analysis. Based on the sensitivity and parametric analysis of the database, it is shown that: compressive strength (f' cc) and axial transition strain (ɛ c1) are more sensitive to FRP lateral stiffness (K l); ultimate axial strain (ɛ cu) is more sensitive to K l -to-unconfined compressive strength (f' co) ratio and fiber ultimate tensile strain (ɛ fu); hoop rupture strain (ɛ h,rup) is more sensitive to fiber elastic modulus (E f); and axial transition strength (f' c1) is more sensitive to f' co. It is also shown that the proposed expressions provided more accurate predictions of the ultimate condition and transition point on the axial stress–strain curve of FRP-confined concrete than the existing expressions. This was achieved by using a larger number of datasets and accurately capturing the effects of the most influential input parameters in the proposed expressions. [ABSTRACT FROM AUTHOR]

• A database containing the intact rock properties was prepared for failure mechanism detection. • A new hybrid GEP-based logistic regression (GEP-LR) model was proposed as a multi-class classifier. • The propensity of over-stressed rock masses to different failure mechanisms was measured with high accuracy. • The proposed model was prepared as a MATLAB code for further applications. The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEP-LR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies. [ABSTRACT FROM AUTHOR]

Deep underground excavations in mining and civil engineering are subjected to high in-situ stresses which can cause rockburst. Rockburst is an instantaneous release of a large amount of strain energy stored in rockmass that can lead to injuries, deaths, and damage to infrastructures. Many studies have been done regarding rockburst, however, there is no practical model to predict the stress level that rockburst occurs (i.e. maximum rockburst stress) and its related risk (i.e. rockburst risk index) based on real rockburst tests, and the main rock mechanical properties. In this study, a comprehensive database of true-triaxial unloading tests on rocks having a wide range of properties was compiled. The agglomerative hierarchical clustering (AHC) analysis was carried out on the original database to evaluate the presence of natural groups and outliers. Then, the stepwise selection and elimination (SSE) procedure were employed for dimension reduction of the problem and identifying the most influential attributes on rockburst parameters. Afterward, two robust non-linear algorithms, including gene expression programming (GEP) and classification and regression tree (CART) were used to develop the predictive models for rockburst maximum stress and its risk index. The validation verification of the proposed models using several indices proved the high prediction performance of the developed non-linear models. Finally, a parametric analysis was carried out to evaluate the influence of each input parameter on the corresponding output. The proposed models in this study are practical and do not require any presupposition about rockburst mechanism, which makes them be used easily in practice by engineers at the design and progress stages of the underground projects. [ABSTRACT FROM AUTHOR]