Uniaxial compressive strength (UCS) is a critical geomechanicalparameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests.In particular, a user-friendly program was developed based on the GEP model to enable rapidand very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments

The assessment of the energy required for crack propagation in concrete structures has been fascinating since fracture mechanics was applied to concrete. In the case of concrete, considered a quasi-brittle material, the fracture energy has proven to be a crucial factor in the reliable design of structures and modeling failure behavior. However, due to the complex, time-consuming, and expensive laboratory tests, there has been ongoing and intense debate regarding the methods to estimate the fracture energy of concrete. The advent of machine learning (ML) methods in this domain can hold great promise for resolving such issues once and for all. This study used a comprehensive analysis of twelve ML algorithms for estimating the initial fracture energy of concrete (IFEC), utilizing a more extensive and diverse database (500 data points) than previous studies. The performance of the ML models was evaluated using several metrics, such as coefficient of determination (R2) and variance accounted for (VAF). The findings revealed that all the ML models employed in this study demonstrate remarkable accuracy in estimating the IFEC value, with R2 and VAF values of more than 0.86 and 93.10 %, respectively. A ranking of the models based on their estimation accuracy was provided, facilitating the selection of the support vector regression (R2 = 0.9897; VAF = 99.50 %) and long-short-term memory (R2 = 0.9804; VAF = 99.00 %) methods as the most reliable models for IFEC estimation. Both the laboratory test and ML models presented the highest IFEC value for a water-to-cement ratio of 0.35. Additionally, by increasing the values of each of the parameter's maximum size of aggregates (from 7 mm to 35 mm) and the specimen’s age (from 3 days to 180 days), the IFEC value was increased by about 100 %. Notably, a user-friendly software based on the ML models was developed, enabling fast and highly accurate estimation of IFEC, thereby eliminating the need for time-consuming and expensive laboratory tests.

Direct determination of shear wave velocity requires time, cost, and high accuracy due to the complexity of the rock texture. In current research statistical and intelligent approaches have been used to predict the shear wave velocity of rock samples. Also, a new correlation between dynamic and static rock properties was established and the shear wave velocity was estimated based on index tests using Gaussian process regression, multivariate linear regression, feedforward back-propagation artificial neural network, and K-nearest neighbor methods. In total, 120 data related to limestone and sandstone samples of the main projects were used for modeling. Water absorption, compressional wave velocity, and density were used as inputs. The outcomes revealed that the PW/SW ratio is equal to 1.69. Various statistics were used to check the method results. The statistical results showed that it is possible to forecast Ed, and SW with high accuracy. Also, the precision of the GPR was higher than the FBP-ANN, statistical analysis, and KNN. Estimation of SW by GPR showed R of 0.992, and RMSE of 0.06, respectively. These four methods were able to estimate the SW with a mean variation percentage of +0.19%. It's important to consider that these models are best suited for predicting SW when the predictor indicators fall within the same range as this study.

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms.

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (alpha), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

In this study, twelve machine learning (ML) techniques are used to accurately estimate the safety factor of rock slopes (SFRS). The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran, evenly distributed between the training (80%) and testing (20%) datasets. The models are evaluated for accuracy using Janbu's limit equilibrium method (LEM) and commercial tool GeoStudio methods. Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS (MSE = 0.0182, R2 = 0.8319) and shows high agreement with the results from the LEM method. The results from the long-short-term memory (LSTM) model are the least accurate (MSE = 0.037, R2 = 0.6618) of all the models tested. However, only the null space support vector regression (NuSVR) model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant. It is suggested that this model would be the best one to use to calculate the SFRS. A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties. In this study, we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

The present study focuses on modeling the solid oxide fuel cell power plant combined with an absorption-ejection refrigeration cycle. First, a comparison is made between the absorption chiller refrigeration cycle and the absorption-ejection chiller to connect the superior cycle to the solid oxide fuel cell as an auxiliary cycle. Then, the solid oxide fuel cell cycle, the combustion of the output product, the heat recovery unit combined with the refrigeration cycle, and freshwater production are modeled. Next, the sensitivity analysis is presented in order to study the effect of the design parameters on objective functions, which simplifies the justification of the optimization results based on the genetic algorithm. In order to perform optimization, machine learning methods have been employed to reduce computational time and cost. The optimization of this cycle shows that the exergy efficiency is enhanced up to 68%, whereas the overall cost rate is in within 9.7–10.4 dollars per hour.

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufacturedsand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

Accurately predicting rockburst in deep tunnels is paramount, as it ensures the utmost safety, minimizes costs and delays, and optimizes design and construction processes. In this paper, the efficacy of six machine learning (ML) methods was evaluated to forecast this phenomenon through the evaluation of ejection velocity (Vmax) and kinetic energy (Kmax) of failed rocks. The higher the values of Vmax and Kmax, the more favorable the conditions for rockburst. 300 datasets were generated in the Abaqus software for training and testing the ML models. Through a comprehensive analysis of the results, the potential of ML models to predict the rockburst was unequivocally affirmed. Both numerical simulations and ML models demonstrated that an elongated weak plane strategically positioned at a distance equivalent to the tunnel's radius from its perimeter and inclined at a precisely calculated angle of 45 degrees exerted the most significant influence on the rockburst.

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early -stage decision -making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning -based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting -edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

Biomass-derived power generation presents various potential benefits compared to conventional fossil fuel-based power generation. Also, efficient integration of energy systems leads to higher sustainability and effectiveness. This paper presents a biomass-fueled energy system with two open and closed Brayton cycles in the waste heat, of which a humidification and dehumidification system is implemented. The usage of four different biomasses as the fuel of the gasifier is tested and put to comparison from the aspects of thermodynamics, economics, and environment. The Grassman diagram for each layout is presented to signpost the exact point of highest exergy destruction and irreversibility. The optimization based on machine learning techniques is conducted to pinpoint the exact optimum operational conditions. The results indicate that wood offers more sustainability compared to other biomasses, and the amount of energy efficiency, exergy efficiency, and freshwater flow rate produced for Wood biomass in the proposed system are 75.81 %, 36.98 %, and 0.4091 kg/s, respectively. Also, two optimization scenarios have been done for this study. In the initial optimal solution finding, effectivness, unit product cost, and carbon dioxide emission index are equal to 45.9 %, 12.6 $/GJ, and 0.7161 kg/kWh, correspondingly. In the latter scenario, the effectiveness, net power production, and production unit cost of products are equal to 45.9 %, 6580 kW, and 12.61 $/GJ, respectively.

In this study, a new algorithm for recommending movies to viewers has been proposed. To do this, the suggested method employs data mining techniques. The proposed method includes three steps for generating recommendations: “preprocessing of user profile information,” “feature extraction,” and “recommendation.” In the first step of proposed method, the user information will be examined and transformed into a form that can be handled in the next phases. In the second step of the proposed method, user attributes are then extracted as a collection of their individual qualities, as well as the average rating of each user for various genres. The bee colony optimization algorithm is then used to select the optimal features. Finally, in the third step of the proposed method, the ratings of similar users are utilized to offer movies to the target user, and the similarities between various users are determined using the characteristics calculated for them, as well as the Euclidean distance criteria. The proposed method was evaluated using the MovieLens database, and its output was assessed in terms of precision and recall criteria; these results show that the proposed method will increase the precision by an average of 1.39% and the recall by 0.8% compared to the compared algorithms.

Complex and intricate preparation techniques, the imperative for utmost precision and sensitivity in instrumentation, premature sample failure, and fragile specimens collectively contribute to the arduous task of measuring the fracture toughness of concrete in the laboratory. The objective of this research is to introduce and refine an equation based on the gene expression programming (GEP) method to calculate the fracture toughness of reinforced concrete, thereby minimizing the need for costly and time-consuming laboratory experiments. To accomplish this, various types of reinforced concrete, each incorporating distinct ratios of fibers and additives, were subjected to diverse loading angles relative to the initial crack (& alpha;) in order to ascertain the effective fracture toughness (Keff) of 660 samples utilizing the central straight notched Brazilian disc (CSNBD) test. Within the datasets, six pivotal input factors influencing the Keff of concrete, namely sample type (ST), diameter (D), thickness (t), length (L), force (F), and & alpha;, were taken into account. The ST and & alpha; parameters represent crucial inputs in the model presented in this study, marking the first instance that their influence has been examined via the CSNBD test. Of the 660 datasets, 460 were utilized for training purposes, while 100 each were allotted for testing and validation of the model. The GEP model was finetuned based on the training datasets, and its efficacy was evaluated using the separate test and validation datasets. In subsequent stages, the GEP model was optimized, yielding the most robust models. Ultimately, an equation was derived by averaging the most exemplary models, providing a means to predict the Keff parameter. This averaged equation exhibited exceptional proficiency in predicting the Keff of concrete. The significance of this work lies in the possibility of obtaining the Keff parameter without investing copious amounts of time and resources into the CSNBD test, simply by inputting the relevant parameters into the equation derived for diverse samples of reinforced concrete subject to varied loading angles.

There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently provide a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper, a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as a benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of the descriptor.

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.

The calculation of the minimum horizontal stress (Shmin) is critical for well planning and hydraulic fracture design. The in-situ Shmin can be estimated via borehole injection tests or theoretical approaches. However, these approaches are complex, costly, need unavailable tectonic stress data, and can only be performed at a specified depth. To that end, this paper intends to apply the most recent varieties of recurrent neural networks (RNNs), such as conventional RNN, long-short-term memory (LSTM), and gated recurrent unit (GRU), to Shmin time-series prediction for the first time. In the models, 13,956 datasets including six input parameters effective on the Shmin from an oil well in Iran were used. 80 percent of the data (from depth 1936 m to depth 3637 m) was used for training, while 20 percent (from depth 3637 m to depth 4068 m) was used for testing. All of its hyper-parameters were extensively adjusted to maximize the accuracy of the RNN models. The performance of the RNN models was compared to that of six other machine learning approaches using various statistical criteria. All of the models demonstrated potential capacity to forecast Shmin. However, for Shmin prediction in oil wells, the GRU model with 700 epochs, 32 hidden neurons, 8 batch sizes, 3 hidden layers, ReLU activation function, 6 time series length, Nadam optimization algorithm, and 0.5 dropout rate was recommended. It is possible to reduce significantly the time and costs associated with measuring the Shmin in this manner.

Many environmental issues have arisen as a result of the widespread usage of concrete, which has led to a reduction of river sand. The excessive extraction of river sand has led to various negative consequences, such as ecosystem disruption, groundwater depletion, coastal erosion, and biodiversity loss. Manufactured sand (MS) from waste deposits may be used in lieu of river sand to address this problem. In this study, to facilitate the production of manufactured sand concrete (MSC), the potential of twelve machine learning (ML)-based models was examined. These models were trained and tested on 248 and 62 laboratory datasets containing nine features effective on the mechanical properties of MSC. MSC's splitting tensile strength (STS) was considered the model's target. The influences that the water-to-cement (W/C) ratio, the stone nano-powder content (SNPC), and the curing age (CA) have on the STS of MSC were also analyzed. Detailed analysis of the results revealed that all the well-tuned ML models have acceptable potential for estimating the STS of MSC; however, the extra tree regressor (ETR) model was in the highest agreement with the laboratory results. Both the ML and laboratory findings showed that MSC with 10% SNPC benefits the long-term STS of MSC. A graphical user interface for the ML-based models was also developed to further aid in the estimation of STS for engineering challenges. The proposed models can be a suitable alternative to time-consuming, expensive, and complex laboratory methods to facilitate the MSC production.

There are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over-estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.

As nanotechnology developed, new materials emerged that might be employed to improve the mechanical properties of materials like concrete. Intensity factors are proportional to the kind of nanomaterial and its concentration. There is not yet a reliable prediction model for the uniaxial compressive strength (UCS) of nanomaterial-reinforced concrete. Access to such models is crucial for developing and evaluating nanomaterial-reinforced concrete structures. This study investigated the potential of eleven well-known machine learning (ML) algorithms to determine the most accurate and suitable ones to estimate the UCS of nanosilica-reinforced concrete. For this purpose, 460 data points were collected from experimental tests, including five input parameters and one target (UCS). The considered input parameters are the percentage of nanosilica in concrete cement (NS), sample diameter (D), sample length (L), porosity (n), and P-wave velocity (Vp). 80% of the data points were used for training and 20% for testing. The models were then verified using statistical analysis, and their behavior was compared to that in practice. Statistical analysis showed that all the models have achieved good accuracy compared to the experimental results, so the model with the lowest accuracy was the decision tree regressor (DTR) model, with a correlation coefficient of 0.68. However, comparing the behavior of the models with the practice mode by changing the value of one of the input parameters while keeping the values of other parameters constant showed that only the support vector regression (SVR) and null space SVR (NuSVR) models behave correctly. These two models were proposed as the most suitable for estimating the UCS of nanosilica-reinforced concrete. To further aid in the estimation of the UCS of nanosilica-reinforced concrete for engineering challenges, a graphical user interface (GUI) for the ML-based models was developed.