• Smart Structures and Systems
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• 제29권4호
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.113-130
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• 2022

Sophisticated automation-based electronics world, more electrical and electronic devices are being used by people from different regions across the universe. Different manufacturers and vendors develop and market a wide variety of power generation and utilization devices under different operating parameters and conditions. People use a variety of appliances which use electrical energy as power source. These appliances or gadgets utilize the generated energy in different ratios. Night time the utilization will be less when compared with day time utilization of power. In industrial areas especially mechanical industries or Heavy machinery usage regions power utilization will be a diverse at different time intervals and it vary dynamically. This always causes a fluctuation in the grid lines because of the random and intermittent use of these apparatus while the power generating apparatus is made to operate to provide a steady output. Hence it necessitates designing and developing a method to optimize the power generated and the power utilized. Lot of methodologies has been proposed in the recent years for effective optimization and economical load dispatch. One such technique based on intelligent and evolutionary based is Black Widow Optimization BWO. To enhance the optimization level BWO is hybridized. In this research BWO based optimize the load for multi area is proposed to optimize the cost function. A three type of system was compared for economic loads of 16, 40, and 120 units. In this research work, BWO is used to improve the convergence rate and is proven statistically best in comparison to other algorithms such as HSLSO, CGBABC, SFS, ISFS. Also, BWO algorithm best optimize the cost parameter so that dynamically the load and the cost can be controlled simultaneously and hence effectively the generated power is maximum utilized at different time intervals with different load capacity in different regions of utilization.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Geomechanics and Engineering
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• 제32권3호
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• pp.271-291
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• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Steel and Composite Structures
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• 제49권1호
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• pp.91-107
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• 2023

An effective approach to promoting sustainability within the construction industry is the use of recycled aggregate concrete (RAC) as a substitute for natural aggregates. Ensuring the frost resilience of RAC technologies is crucial to facilitate their adoption in regions characterized by cold temperatures. The main aim of this study was to use the Random Forests (RF) approach to forecast the frost durability of RAC in cold locations, with a focus on the durability factor (DF) value. Herein, three optimization algorithms named Sine-cosine optimization algorithm (SCA), Black widow optimization algorithm (BWOA), and Equilibrium optimizer (EO) were considered for determing optimal values of RF hyperparameters. The findings show that all developed systems faithfully represented the DF, with an R² for the train and test data phases of better than 0.9539 and 0.9777, respectively. In two assessment and learning stages, EO - RF is found to be superior than BWOA - RF and SCA - RF. The outperformed model's performance (EO - RF) was superior to that of ANN (from literature) by raising the values of R² and reducing the RMSE values. Considering the justifications, as well as the comparisons from metrics and Taylor diagram's findings, it could be found out that, although other RF models were equally reliable in predicting the the frost durability of RAC based on the durability factor (DF) value in cold climates, the developed EO - RF strategy excelled them all.

• Steel and Composite Structures
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• 제50권4호
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Geomechanics and Engineering
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• 제32권6호
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.