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Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time. [ABSTRACT FROM AUTHOR]

This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of Gene Expression Programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next Generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of "change lane" with 92.98% accuracy, and "do not change lane" with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymers (FRP) have been widely used in shear strengthening applications of reinforced concrete (RC) beams. The accurate prediction of the FRP contribution to the shear strength of beams is essential for reliable design. Gene expression programming (GEP) has been widely utilized because it reliably expresses complex relationships between experimental variables. In this study, three new GEP models are proposed for three different strengthening configurations of FRP such as fully-wrapping, U wrapping, and side-bonding to predict the FRP contribution to shear strength. These models are developed using the most comprehensive database containing a total of 811 strengthened beams (350 fully-wrapped, 328 U-wrapped, and 133 side-bonded. Many variables have been considered in the proposed GEP models, including those that have been experimentally effective but are often neglected in existing literature equations, such as the shear span-to-effective depth ratio (a / d) and the stirrup ratio ( ρ w ). Additionally, the reliability of existing equations in the literature and the proposed GEP models for predicting the FRP contribution to shear strength was statistically evaluated. As a result of this evaluation, the proposed GEP models for each strengthening configuration of FRP yielded the most accurate statistical results, with the lowest coefficient of variation (COV), and the highest coefficient of correlation (R). [ABSTRACT FROM AUTHOR]

This study aims to develop semi-empirical formulas for foretelling the buckling loads of clamped and simply supported rectangular isotropic panels with central circular perforation exposed to different ratios of biaxial load conditions. The empirical formulas were developed and evaluated using Gene Expression Programming (GEP), Artificial Neural Network (ANN), and Finite Element Method (FEM). A total of 714 data set, generated using the FEM, is used to establish and validate the empirical formulas. This study investigates the effect of perforation sizes, plate aspect ratios, and biaxial load ratios on the buckling strength of perforated panels. The proposed formulas will allow a quick and easy estimation of buckling loads for perforated rectangular panels with acceptable accuracy without the need for sophisticated calculations. The results of the empirical formulas were comparable reasonably well with the results of the finite element analysis (FE) and available literature findings. [ABSTRACT FROM AUTHOR]

This paper introduces innovative approaches to enhance and develop one-equation RANS models using gene-expression programming. Two distinct strategies are explored: overcoming the limitations of the Boussinesq hypothesis and formulating a novel one-equation turbulence model that can accurately predict a wide range of turbulent wall-bounded flows. A comparative analysis of these strategies highlights their potential for advancing RANS modeling capabilities. The study employs a single-case CFD-driven machine learning framework, demonstrating that machine-informed models significantly improve predictive accuracy, especially when baseline RANS predictions diverge from established benchmarks. Using existing training data, symbolic regression provides valuable insights into the underlying physics by eliminating ineffective strategies. This highlights the broader significance of machine learning beyond developing turbulence closures for specific cases. [ABSTRACT FROM AUTHOR]

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement's dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements. [ABSTRACT FROM AUTHOR]

The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C–S) of FR-SCC using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses. [ABSTRACT FROM AUTHOR]

The progression of optical materials and their associated applications necessitates a profound comprehension of their optical characteristics, with the Judd–Ofelt (JO) theory commonly employed for this purpose. However, the computation of JO parameters (Ω2, Ω4, Ω6) entails wide experimental and theoretical endeavors, rendering traditional calculations often impractical. To address these challenges, the correlations between JO parameters and the bulk matrix composition within a series of Rare-Earth ions doped sulfophosphate glass systems were explored in this research. In this regard, a novel soft computing technique named genetic expression programming (GEP) was employed to derive formulations for JO parameters and bulk matrix composition. The predictor variables integrated into the formulations consist of JO parameters. This investigation demonstrates the potential of GEP as a practical tool for defining functions and classifying important factors to predict JO parameters. Thus, precise characterization of such materials becomes crucial with minimal or no reliance on experimental work. [ABSTRACT FROM AUTHOR]

Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data; for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms. [ABSTRACT FROM AUTHOR]

Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions. [ABSTRACT FROM AUTHOR]

To achieve smart and sustainable construction goals, machine learning (ML) techniques can serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ approaches in parameter estimation essential for infrastructure design. Of these, soil's shear strength parameters, notably cohesion (c) and friction angle (φ), typically govern the design of geo-structures. For quick and cost-effective estimation of these parameters, the earlier studies proposed ML-based predictive models that were less practical and accurate or consider an excessive number of input variables. To minimize these limitations, our study proposes new models of c and φ using gene expression programming (GEP) based on readily available soil attributes such as sand content (S), depth (D), specific gravity (Gs), liquid limit (LL), plastic limit (PL), and fine content (FC). The newly proposed models show excellent accuracy as the values of R2, RMSE (root mean square error), MAE (mean absolute error), RSE (relative standard error) for c-predictive model were 0.984, 1.13, 0.878, 0.017, respectively, and were 0.927, 1.123, 0.922, 0.072, respectively, for φ-predictive model. Through sensitivity analysis, FC and LL emerged as the most critical parameters influencing c, while Gs and PL proved sensitive for determining φ. In comparison with existing models, the c-predictive model displays R2 enhancements of 11.84–45.87% and RMSE improvements of 65.9–92.03%, while the φ-predictive model showcases R2 gains of 13.16–23.75% and RMSE improvements of 58.79–69.29%. By integrating predictive prowess with sustainable and smart construction principles, our study plots a realistic course for efficient geo-structural design. [ABSTRACT FROM AUTHOR]

In the reinforced concrete (RC) columns which are exposed extreme loads such as earthquake effects, the plastic hinge length can be defined as the length of the region where flexural moments exceed the yielding capacity, and the plastic deformations are concentrated. More accurate estimation of plastic hinge length increases the reliability of the seismic design. However, a sensitivity prediction of plastic hinge length is difficult due to a large number of model parameters. Therefore, this study aims to predict the plastic hinge length using the gene expression programming (GEP). An experimental database of 133 RC columns gathered from the literature was utilized for prediction with GEP. The results of GEP model are statistically compared with those of 13 models existing in the literature proposed by various researchers. The comparison results reveal that the proposed GEP-based formulation has the best efficiency among all models. Furthermore, a sensitivity analysis and parametric study are conducted to identify the most influential parameters affecting the GEP formulation. [ABSTRACT FROM AUTHOR]

Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks. [ABSTRACT FROM AUTHOR]

In this study, three artificial intelligence models, namely group method of data handling, gene expression programming and random forest, are proposed to predict the ultimate bearing capacity of concrete filled steel tube stub columns. A total of 220 data samples collected from the literature was used to construct the three models. Five statistical indices were used to evaluate the performance of the three models and the other five existing design codes and two reference models. Compared with the optimal model among the seven existing models, the coefficient of variation, mean absolute percentage error, root relative squared error and integral of absolute error values of all datasets of the three models (i.e., group method of data handling, gene expression programming and random forest) were decreased by 46.47%, 56.49%, 71.35% and 65.78%; 49.82%, 59.49%, 66.88% and 64.51%; 79.27%, 84.83%, 78.64% and 83.71%, respectively; while the determination coefficient values of all datasets of the three models were increased by 7.36%, 6.79% and 8.22%, respectively. The results show that the predicted results of the three models agree well with the experimental results, and the random forest model has the best comprehensive performance. [ABSTRACT FROM AUTHOR]

Utilizing the aggregate piers is one of the methods to improve and increase the bearing capacity of soft soils. The ultimate bearing capacity of these piers is affected by parameters such as the physical properties of the piers, structural conditions, the embedment depth and replacement ratio of piers, which complicates the estimation of bearing capacity. In this study, the Gene Expression Programming method was used for the prediction of the ultimate bearing capacity of clay soils reinforced with aggregate piers. For this purpose, two different models were developed, of which the first model (GEP2) utilized two input variables, the undrained shear strength of clay (Su) and replacement ratio (ar), while the second model (GEP4) used four input variables including the undrained shear strength of clay (Su), replacement ratio (ar), slenderness ratio (Sr), and embedment depth of piers (df). The coefficient of determination of the GEP2 model, and the GEP4 model is 0.921 and 0.942, respectively. Besides, comparing the GEP4 model of this research with the developed models of previous studies confirms the superior performance of the GEP4 model, considering both the accuracy and number of input parameters. The results of sensitivity analysis showed that the undrained shear strength of clay (Su), replacement ratio (ar), slenderness ratio (Sr), and embedment depth of piers (df) have the highest impact on the prediction of bearing capacity, respectively. Furthermore, the parametric analysis demonstrated that increasing the Su, ar, Sr, and df would improve the bearing capacity of the aggregate piers reinforced clay. [ABSTRACT FROM AUTHOR]

Under fatigue loading, the interfacial fatigue life of fiber-reinforced polymer(FRP)–concrete is an important index for the analysis of the fatigue performance of reinforced concrete beams strengthened with FRP materials and the evaluation of the reinforcement effect. To solve the problems of the inconsistent and limited accuracy of existing fatigue life prediction models, gene expression programming (GEP) was used to study the interfacial fatigue life of FRP–concrete. Firstly, 219 sets of interfacial fatigue test data were collected, which included two kinds of reinforcement methods, namely, externally bonded (EB) reinforcement and near-surface-mounted (NSM) reinforcement; secondly, Pearson correlation analysis was used to determine the key factors affecting the fatigue life, and then GEP was used to explore the influence of different input forms on the prediction accuracy of the model. Fatigue life calculation formulas applicable to the two kinds of reinforcement methods, i.e., EB and NSM, were established, and a specific calculation formula was established. The model was subjected to parameter sensitivity analysis and variable importance analysis and was found to reflect the intrinsic relationship between the fatigue life and various factors. Finally, the GEP model was compared with the models proposed by other researchers. Five statistical indices, such as the coefficient of determination and the average absolute error, were selected to assess the model, and the results show that the GEP model has higher prediction accuracy than other models, with a coefficient of determination of 0.819, and indicators such as the average absolute error are also lower than those of the rest of the models. [ABSTRACT FROM AUTHOR]

Geotechnical engineers must place a high priority on the analysis and forecasting of slope stability to prevent the disasters that can result from a failed slope. As a result, it is crucial to accurately estimate slope stability in order to ensure the project's success. This sort of information is indispensable in the early stages of concept and design, when important decisions must be made. In this study, an optimized GEP-based model for calculating the safety factor of rock slopes (SFRS) was proposed. For this purpose, a variety of rock slopes for circular failure mode were analyzed using the PLAXIS software to generate 325 datasets. In the datasets, six effective parameters on the SFRS including unit weight, friction angle, slope angle, cohesion, pore pressure ratio, and slope height were considered. 80% of the datasets were used for training and 20% for test. As a result of finding the optimal fit between the predictions, an equation for the refined GEP model was derived. Finally, the equation's potential ability to estimate SFRS was approved by comparing its outputs with the actual ones and comparing its behavior with practice. The mutual information sensitivity analysis revealed that the unit weight parameter is the most influential variable in the proposed equation. This model can reduce the uncertainties about the stability of rock slopes and give machine learning development in the field. [ABSTRACT FROM AUTHOR]

Liquefaction in soil layers is a vital factor intensifying earthquake damages.This study compares the numerical evaluation process and the results of two methods based on stress and energy. It is inferred from the calculations that the stress-based method predicts a higher liquefaction potential with a lower safety factor as it promises liquefaction in deeper soil layers. In return, liquefaction tends to occur at a shallower depth with higher intensity in the energy-based method. Through applying the two approaches based on the data collected from different areas around Tabriz, a liquefaction-zoning map is presented. Despite being far from the fault, the central to the western and the southwestern parts of Tabriz has a high liquefaction potential. Eventually, based on the evaluated liquefaction potential using the stated methods and adopting the gene expression programming (GEP) approach, an equation is introduced to estimate the liquefaction potential for the case study. The predictions of the proposed models were consistent with the findings of experimental methods, demonstrating appropriate statistical measures and parametric analysis. It can also be concluded from the results of the parametric analysis, that the parameters of maximum acceleration, earthquake magnitude, and SPT number have more great impact on soil liquefaction. [ABSTRACT FROM AUTHOR]

The creep index plays an important role in calculating the long-term settlement of natural soft clays, so it is vital to determine the creep index quickly and accurately. However, the prediction accuracy of the existing creep index models is low. This study presents seven gene expression programming (GEP) models by using different combinations of the liquid limit wL, plasticity index Ip, void ratio e and clay content CI as input variables for the prediction of creep index. A total of 151 datasets were collected from the available literature for building and testing the GEP models. The proposed GEP models were compared with two machine learning (ML) models (i.e., back propagation neural network and random forest) and five conventional empirical models in terms of three statistical indicators. The research results showed that the prediction performances of the two proposed GEP models (i.e., with combinations C I - w L - e and C I - I p - w L - e as input, respectively) surpass those of the five conventional empirical models and two ML-based models, recommended for predicting the creep index of natural soft clays in engineering practice. [ABSTRACT FROM AUTHOR]

The building and construction industry's demand for steel reinforcement bars has increased with the rapid growth and development in the world. However, steel production contributes to harmful waste and emissions that cause environmental pollution and climate change-related problems. In light of sustainable construction practices, bamboo, a readily accessible and eco-friendly building material, is suggested as a viable replacement for steel rebars. Its cost-effectiveness, environmental sustainability, and considerable tensile strength make it a promising option. In this research, hybrid beams underwent analysis through the use of thoroughly validated finite element models (FEMs), wherein the replacement of steel rebars with bamboo was explored as an alternative reinforcement material. The standard-size beams were subjected to three-point loading using FEMs to study parameters such as the load–deflection response, energy absorption, maximum capacity, and failure patterns. Then, gene expression programming was integrated to aid in developing a more straightforward equation for predicting the flexural strength of bamboo-reinforced concrete beams. The results of this study support the conclusion that the replacement of a portion of flexural steel with bamboo in reinforced concrete beams does not have a detrimental impact on the overall load-bearing capacity and energy absorption of the structure. Furthermore, it may offer a cost-effective and feasible alternative. [ABSTRACT FROM AUTHOR]

In this investigation, we aimed to address the pressing challenge of treating osteosarcoma, a prevalent and difficult-to-treat form of cancer. To achieve this, we developed a quantitative structure-activity relationship (QSAR) model focused on a specific class of compounds called 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives. A set of 39 compounds was thoroughly examined, with 31 compounds assigned to the training set and 8 compounds allocated to the test set randomly. The goal was to predict the IC50 value of these compounds accurately. To optimize the compounds and construct predictive models, we employed a heuristic method of the CODESSA program. In addition to a linear model using four carefully selected descriptors, we also developed a nonlinear model using the gene expression programming method. The heuristic method resulted in correlation coefficients (R²) of 0.603, 0.482, and 0.107 for R²cv and S², respectively. On the other hand, the gene expression programming method achieved higher R² and S² values of 0.839 and 0.037 in the training set, and 0.760 and 0.157 in the test set, respectively. Both methods demonstrated excellent predictive performance, but the gene expression programming method exhibited greater consistency with experimental values. The successful nonlinear model generated through gene expression programming shows promising potential for designing targeted drugs to combat osteosarcoma effectively. This approach offers a valuable tool for optimizing compound selection and guiding future drug discovery efforts in the battle against osteosarcoma. [ABSTRACT FROM AUTHOR]

The head–discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA–GEP–SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6% and 1.7%, respectively. The root mean square error was lowered by 24.8%, which is significant. The number of datasets with an error of less than 10% and 20% was increased by 15% and 12%, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues. [ABSTRACT FROM AUTHOR]

The multi-skill resource-constrained project scheduling problem (MS-RCPSP) is a significant management science problem that extends from the resource-constrained project scheduling problem (RCPSP) and is integrated with a real project and production environment. To solve MS-RCPSP, it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme. This paper proposes an improved gene expression programming (IGEP) approach to explore newly dispatching rules that can broadly solve MS-RCPSP. A new backward traversal decoding mechanism, and several neighborhood operators are applied in IGEP. The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process, and improves the algorithm’s performance. Several neighborhood operators improve the exploration of the potential search space. The experiment takes the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset as the training set and testing set of IGEP. Ten newly dispatching rules are discovered and extracted by IGEP, and eight out of ten are superior to other typical dispatching rules. [ABSTRACT FROM AUTHOR]

Using bolt shear connectors in Steel-Concrete-Steel (SCS) slabs is very important due to producing a complete steel plates connection and adjusting the sandwich thickness desirably. Therefore, in the present research, a numerical study is conducted on the flexural behavior of SCS sandwich slabs with stud-bolt shear connectors under the effect of the quasi-static concentrated load. For this purpose, first, the experimental specimens extracted from the previously published study were numerically modeled and quasi-statically analyzed using explicit dynamic analysis. Then based on the tests, the models were validated. Subsequently, the effect of the parameters, including the thickness of steel plates, stud-bolts diameter, the concrete core thickness, center-to-center distance of stud-bolt connectors, and the concrete core strength was evaluated based on the numerical models on the failure modes and the force-displacement curve. Finally, using the experimental setup and gene expression programming (GEP) algorithm, several numerical models were planned to predict the maximum strength of the slabs and a simple relation was proposed. The maximum strength resulting from the proposed relation and numerical models had an acceptable agreement with an error of 11% based on mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel model for predicting setup of closed-ended steel pipe (CEP) piles driven in predominantly fine-grained soils using gene expression programming (GEP). The proposed GEP model incorporates both pile and soil properties and is based on dynamic pile load test data obtained from 109 piles installed at 59 different sites. Multiple variable regression analyses conducted on the compiled dataset showed that the most influential parameters in predicting the time-dependent resistance of driven piles were the resistance mobilized at the end of pile installation, time elapsed after installation, pile shaft surface area, and average silt content along the pile length. The data were divided into a training set for model calibration and a validation set for model verification. Sensitivity analysis was performed to assess the model's robustness. A comparison of the new GEP model with existing pile setup models in the literature was carried out using the collected data, which demonstrated that the proposed GEP model significantly outperformed the tested models. Additionally, data from 22 additional CEP piles were compiled from the literature for model verification purposes. The results showed that the proposed GEP model can predict the total pile resistance with good accuracy. [ABSTRACT FROM AUTHOR]

In the present work, a robust prediction model is presented for predicting Ni-W coating microhardness using powerful soft computing techniques, i.e., Gene expression programming (GEP). We considered the W content in coating (wt.%), pH, time (min) of coating bath, frequency (kHz) and current density (mA cm−2) of pulse current electrodeposition as input variables and the microhardness of Ni-W coatings as output variables. To achieve this, we had three main steps: (i) 63 experiments were collected to construct models; (ii) Creating training and testing phases based on 50 and 13 data and (iii) A new model is built and compared using correlation coefficients (R2), root relative square errors (RRSE), and relative standard errors (RSE). Using the results, GEP-6 was found to be the appropriate model for predicting the microhardness of Ni-W coatings with R2 = 0.9926, RSE = 0.0077, and RRSE = 0.0880. Additionally, sensitivity analysis results indicated that pulse electrodeposition's frequency, current density, and time of coating bath were the most effective parameters in determining microhardness. Comparison of actual (experimented) versus predicted microhardness for Ni-W coatings using GEP-2, GEP-4, and GEP-6 models in (a) training, and (b) testing. [ABSTRACT FROM AUTHOR]

In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems. [ABSTRACT FROM AUTHOR]

Landslides have claimed many lives and caused significant economic losses in recent years. Therefore, assessing the potential risk for landslide hazard prevention and mitigation is vital and the landslide travel distance is a key aspect in this process. In this study, the group method of data handling (GMDH) was used to predict the landslide travel distance. A total of 111 landslide datasets collected from the literature were used to construct and validate the proposed GMDH model. Five parameters were selected as the input parameters, including initial slope angle, body height, average body thickness, the logarithm of source volume, and body aspect ratio. The GMDH model was compared with the gene expression programming (GEP) model as well as four empirical models proposed by other researchers. The comparison results demonstrated that the predicted results of the GMDH model agreed well with the measured values, with a correlation coefficient of 0.9123, which was higher than the GEP model and the four empirical models. It was concluded that the proposed GMDH model had great potential in estimating the landslide travel distance. [ABSTRACT FROM AUTHOR]

Soil temperature (ST) is one of the vital parameters of soil, which can affect many of the processes occurring in soil. Accurate techniques are therefore needed to know the ST regime at different soil depths. The present study strived to model the daily ST of the Isfahan and Rasht stations, Iran, at four multiple depths (5, 10, 50, 100 cm). To achieve this goal, the standalone gene expression programming (GEP) and support vector machine (SVM) models were applied. In this context, two types of input patterns were taken into consideration including weather parameters- and lagged ST data-based patterns. Additionally, two different pre-processing methods comprising of entropy and τ-Kendall were utilized to discern the most important weather parameters under the weather parameters-based patterns. It was found that both the employed pre-processing techniques introduced various input combinations as the most important weather parameters. In general, entropy-based GEP and SVM models slightly presented higher accuracy compared to the τ-Kendall-based standalone models at different depths. Then, wavelet (W)-based coupled models were proposed by hybridizing the W theory on the GEP and SVM in order to develop the hybrid W-GEP and W-SVM approaches. The outcomes demonstrated that the implemented coupled models outperformed the single GEP and SVM in modeling the ST of various depths under both the defined input patterns. [ABSTRACT FROM AUTHOR]

The current study assesses the collapse sensitivity classes of loess soils using gene expression programming (GEP) and ordinal logistic regression (OLR). The crucial variable to forecast the possible development of loess caves in the Golestan Province (northeast of Iran) is the collapse sensitivity factor (Is). A database of 62 records, including the mechanical and physical characteristics of soils, was used. Oedometer tests were used to estimate the parameters of the collapse coefficient, the time needed for 90% settlement (T90%), and collapse sensitivity. The database includes 10 inputs (grain size, porosity, initial water content, precipitation, climatic data, liquid limit, calcium carbonate content, vegetation, and degree of soil saturation) and one output (collapse sensitivity classes). This is a complicated approach due to the complexity of setting up and performing such kinds of tests in the laboratory. The likelihood of soil classification ranks as severe, moderately severe, moderate, and small sensitivity was inspected using OLR and GEP. This study demonstrated that the OLR approach could effectively differentiate among more than 70% of distinct groups. Furthermore, experimental data reported from Semnan, Sarakhs, and Mashhad also attests to the accuracy of the OLR model. The sensitivity analysis indicated that silt fraction imparts the maximum effect on the collapse sensitivity classes. The trial-and-error method was used to determine the configurations of the GEP model prior to developing an ideal model. The performance of the GEP model to estimate the collapse sensitivity categories in a trustworthy, strong, and useful way is well documented by comparison between the results of the GEP and the experimental findings, which are affordable. [ABSTRACT FROM AUTHOR]

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data; preprocessing all the collected data to obtain an initial sample set; constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The Gene Expression Programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data. [ABSTRACT FROM AUTHOR]

In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO2, and Al hybrid composites were manufactured in the research utilizing the powder metallurgy (PM) technique. A model was created to predict the tribological behavior of SiC–ZrO2–Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R2), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1). [ABSTRACT FROM AUTHOR]

In this study, based on compiled numerical-experimental datasets, important variables, including porosity, density, compressional and shear wave velocity, were utilised to develop a novel model for predicting coal permeability with high accuracy. Multiple linear regression, response surface methodology and gene expression programming were employed to develop empirical models based on the foregoing variables. The performance of the developed models was evaluated using statistical indices. The results showed that the GEP-based model has the best prediction performance compared with other techniques. The proposed GEP-based model with its explicit structure can be readily used in practical applications by field engineers. [ABSTRACT FROM AUTHOR]

Frequent entry in the tunnel boring machine cutterhead for disc cutter wear inspection is a risky, time-consuming, and labor-intensive activity. Existing disc cutter wear prediction models mainly focus on cutter consumption before construction, and it is impossible to estimate the wear of a single cutter when they are applied to on-site construction. To solve this problem, this research presents a method for estimating the wear of each disc cutter on the cutterhead in real time by only using several monitored machine parameters. Firstly, a novel health index that can characterize the wear of each disc cutter is constructed, and the field parameters that have greater impact on the health index are selected. Then, the explicit mathematical expression between the selected parameters and the health index is established based on genetic expression programming. Finally, the on-site data collected from an Indian subway tunnel were used to validate the effectiveness and superiority of the proposed method. The results show that the proposed method can estimate the wear of each disc cutter in real time only by monitoring the rotational speed of cutterhead and tunneling speed. Its average accuracies on the validation set and test set are 90.6% and 85.9%, respectively. Compared with the ridge regression, decision tree, support vector regression and k-nearest neighbors, its accuracy on the test set is 13.0%, 12.7%, 11.2%, and 15.4% higher, respectively. Therefore, the proposed method can greatly reduce the cost for cutter inspection, and the explicit model can be easily deployed to the construction site. [ABSTRACT FROM AUTHOR]

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results. [ABSTRACT FROM AUTHOR]

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature. [ABSTRACT FROM AUTHOR]

Good predictive models should feature the capability of obtaining accurate results through easy and accessible methods in a timely manner. Following a "golden triangle" connecting the accuracy, complexity and applicability of each model, this work employed gene expression programming (GEP) algorithm to develop models for four typical distresses of asphalt pavements in the state of Idaho, respectively. The models developed in this work retain and combine advantages of empirical models and machine learning models and thus serve as promising alternatives to mechanistic–empirical (ME) models. Models with explicit forms were selected and calibrated by GEP, in an intelligent and automatic way. This work also proposes a variable selection method featuring principal component analysis coupled with correlation analysis, and this method was utilized in data pre-processing for construction of simple models with precise model form. Furthermore, this work introduces a systematic and comprehensive approach to evaluation of model accuracy, robustness and sensitivity, based on various statistical methods. Compared with regression models and ME models, the models generated by GEP achieved higher and more stable accuracy. Finally, we present the trade-off between model accuracy and rationality and point out the importance and necessity of reaching a balance in the golden triangle. [ABSTRACT FROM AUTHOR]

Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods. [ABSTRACT FROM AUTHOR]

• The proper dataset for training the GEP is developed by FRAPCON program. • A two-step sensitivity analysis is done to select the most influential parameters. • The GEP gives a set of correlations for fission gas fractions and hydrogen concentration. • The GEP could correlate input parameters successfully with high predictability. This study aims to develop correlations for predicting the fission gas fractions and hydrogen gas concentration during a fuel cycle, using gene expression programming as an evolutionary machine learning approach. The well-known FRAPCON code is used for generating a straightforward dataset under steady-state conditions. The two-step sensitivity analysis is carried out to identify the most influential parameters for correlation development. Wilks' statistical method is used to generate 59 scenarios to distribute input parameter uncertainties evenly, which leads to a confidence level of 95 %. The mean squared error for xenon, krypton, and helium is 0, while hydrogen exhibited a value of 59.36 since fraction values are between 0 and 1 and concentration ranged from 5 PPM to 200 PPM. R 2 values exceeded 0.97, indicating strong correlation accuracy. The high accuracy achieved from the correlations demonstrates that selecting a 59-sample dataset based on Wilk's method is sufficient to obtain accuracy exceeding 95 %. [ABSTRACT FROM AUTHOR]

The accurate prediction of the equivalent hydraulic aperture (EHA) in a single fracture is of paramount importance for the investigation of fracture flow capacity. Previous studies primarily relied on fitting experimental data to obtain the EHA, failing to accurately characterize the impact of the strong nonlinearity among fracture geometric parameters on EHA. This research integrates 170 datasets of measured EHA, incorporating key geometric parameters: average mechanical aperture (bm), peak roughness height (ξ), and the joint roughness coefficient (JRC). These three main geometric characteristic parameters, used as input parameters, are the primary factors influencing the EHA. The Gene Expression Programming (GEP) method was utilized to construct a model for predicting EHA (bh). The developed GEP model was then compared with 6 existing empirical models, as well as 2 AI models (Random Forest (RF) and Support Vector Machine (SVM)). The results indicate that the GEP model (R2 = 0.997) and the SVM model (R2 = 0.9944) both demonstrate high accuracy, evidenced by the GEP model's low RMSE (0.014) and MAE (0.01) values. The sensitivity analysis indicates that in the GEP model, the hydraulic aperture increases linearly with the increase of bm, decreases linearly with the increase of ξ, and initially increases but then decreases as JRC increases. These findings provide insightful contributions to the assessment of hydraulic conductivity in the rough single fracture. [ABSTRACT FROM AUTHOR]

The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. Given environmental benefits, fly ash (FA) is often used as a replacement for Portland cement. In this study, two mechanical properties of concrete with FA, that is, compressive strength f′c and splitting tensile strength fst were modeled by using gene expression programming (GEP). The GEP models were compared with response surface methodology, multiple linear and nonlinear regression. The sensitivity and parametric analysis were also performed. In addition, the influence of parameters of the GEP algorithm on the performance of the developed GEP models was evaluated by testing various linking functions, head sizes, number of genes, number of chromosomes, and fitness functions. It was revealed that the models developed by GEP have high predictive and generalization capability for both f′c and fst of concrete with FA as compared with the other modeling techniques. [ABSTRACT FROM AUTHOR]

Structural lightweight aggregate concrete (SLWAC) can increase the ability of buildings to resist earthquakes by significantly reducing the overall weight of structures. Determining a relationship for assessing the concrete compressive strength of these structures using non‐destructive methods can save both time and money. This study aims to estimate the compressive strength of SLWAC using a model of gene expression programming (GEP) and semi‐direct ultrasonic pulse velocity. A hundred specimens of lightweight concrete were made using clay and natural (mineral) pumice aggregates with the maximum nominal sizes of 12.7 mm (½ inch) and 19.2 mm (¾ inch), respectively. Semi‐direct ultrasonic testing of the specimens was then undertaken in the laboratory. The results show a high correlation coefficient for the equations derived from the GEP. Moreover, the equations obtained from GEP for three types of SLWAC, based on semi‐direct ultrasonic pulse velocity, are compatible with equations obtained from the direct ultrasonic velocity method. The simplicity of generated equations increases the practicality and convenience in the working environment and actual conditions. The R2 (goodness of fit) values of proposed equations for LWA01, LWA02, and LWA03 specimens were 0.912, 0.962, and 0.985, respectively. It was concluded that based on the semi‐direct ultrasonic pulse velocity and derived formulas, the compressive strength of SLWAC can be confidently predicted using GEP. [ABSTRACT FROM AUTHOR]

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) ≥ 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values. [ABSTRACT FROM AUTHOR]

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. The logistic regression (LR) method has been widely used as a data-driven tool for MPM because it is compact and straightforward, and it presents explicit explanations. However, there exists the inherent deficiency of linear assumption in the ordinary LR method. In order to overcome this disadvantage, a hybrid model (i.e., hybrid GEP–LR) that combines LR with gene expression programming (GEP) has been developed. It uses the evolutionary algorithm of GEP to discover automatically the symbolic function defined in the logit transformation of the ordinary LR method without any assumption. To investigate the performance of the hybrid GEP–LR hybrid method, a synthetic data modeling and a case study of prospectivity mapping for porphyry Cu–Mo polymetallic deposits (PCMPDs) in the Eastern Tianshan region, northwestern China are presented here. In the synthetic data modeling, two different datasets, namely linearly separable dataset and nonlinearly separable dataset, were generated by Monte Carlo simulation. By defining the area under the receiver operating characteristic curve (AUC) as a fitness function, these two datasets were used to validate the efficiency of the hybrid GEP–LR method in classification of both linearly and nonlinearly separable datasets. In the case study, 15 hybrid GEP–LR models were designed using 15 different evolutionary strategies of the GEP algorithm including the number of genes, the head length of every gene, and the set of functions. Binary maps of prospectivity for PCMPDs derived using these 15 hybrid GEP–LR models with the fitness function of AUC were compared and contrasted with the one derived using the ordinary LR method. The results demonstrate that the proposed hybrid GEP–LR method is a robust data-driven tool that can deal with both linear and nonlinear problems in data mining. It outperforms the ordinary LR method in predicting target areas for mineral prospecting. GEP provides an inspiring way to combine other machine learning methods in addressing challenges associated with supervised or unsupervised classification issues with respect to complex nonlinearly distributed geodata. [ABSTRACT FROM AUTHOR]

It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and Mwl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R²) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and Mwl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey. [ABSTRACT FROM AUTHOR]

The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models. [ABSTRACT FROM AUTHOR]

The use of soft computing techniques is becoming more common in providing solutions to complex engineering problems such as the concrete breakout strength of anchor. Available techniques include semi-empirical equations that are known to over or underpredict and some soft computing techniques that is incapable of generating predictive equations. This study proposes a gene expression programming (GEP)-based mathematical model to predict the concrete edge breakout capacity of single anchors loaded in shear. In doing so, an experimental database compiled by the American Concrete Institute (ACI) Committee 355, containing 366 samples, was used for the model training and testing. The independent variables considered in the model development are the edge distance, anchor diameter, embedment depth and concrete strength. Moreover, the predictive performance of the developed model was compared to that of the existing models proposed in ACI 318 and the Eurocode 2 (EC2) design standards. The assessment showed that the proposed GEP-based model provided a much more uniform and accurate prediction of the actual strength than the models in the existing design standards. The proposed mathematical model is simple and robust and is expected to be very useful for evaluating the concrete breakout shear capacity of single anchors in pre-planning and pre-design phases; that is, towards inclusions in design standards. [ABSTRACT FROM AUTHOR]

In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength. [ABSTRACT FROM AUTHOR]

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson's coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online. [ABSTRACT FROM AUTHOR]

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19. [ABSTRACT FROM AUTHOR]