Advances in Engineering and Intelligence Systems
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Advances in Engineering and Intelligence Systemsendaily1Wed, 01 Mar 2023 00:00:00 +0330Wed, 01 Mar 2023 00:00:00 +0330Temperature Estimation of Smart Homes with Sensors in Internet of Things Environment based on Block Chain
https://aeis.bilijipub.com/article_169076.html
Today, smart homes are used as one of the most promising communications protocols technologies. The combination of these protocols for communicating with the Internet of Things can carry a remote control. There are a variety of challenges in smart homes one of them is temperature estimation to check green energy consumption. This study tries to use the Internet of Things to estimate the temperature of smart homes, which combines and integrates Z-WAVE into smart homes and 6LoWPAN communication protocols on the Internet of Things. A processor environment has been created as the central base using a blockchain for optimally adapting the ambient temperature of the environment based on a robust controller, which is used in the PID-HTC controller structure of the blockchain to control the temperature accurately. Obtained results show an improvement over the previous method.Maximum dry unit weight and optimum moisture content prediction of lateritic soils using regression analysis
https://aeis.bilijipub.com/article_169077.html
Soils compaction with experimental tests is a pivotal facet in the selection of materials for earth constructions. Due to the time limitations and concerns of finishing resources, it is obligate to develop some relationships for predicting compaction parameters such as maximum dry unit weight (&gamma;_dmax) and optimum moisture content (&omega;_opt) from easily estimated index properties. The purpose is to evaluate the applicability of multivariate adaptive regression splines (MARS) for estimating &gamma;_dmax and &omega;_opt of lateritic soils. Furthermore, different degrees of interactions of models are employed to have comprehensive, precise, and trustable outputs. The outputs of suggested equations to estimate &gamma;_dmax related to modified proctor compaction test provide proper capability in the modeling procedure. In the training dataset, the value of all criteria for MARS-OI-3 is proper, with the value of 0.9365, 0.4146, and 93.647 for R^2, RMSE, and VAF, respectively. But testing phase&rsquo;s results are roughly complicated, where scores of MARS-OI-3 equal to 21, bigger than MARS-OI-2 (10) and MARS-OI-4 (17). In summary, MARS-OI-3 outperforms others, where can be known as the suggested equation. The outputs of suggested equations to estimate &omega;_opt also provide great ability in the modeling. In both phases, the value of all criteria for MARS-OI-2 is proper than MARS-OI-1. Also, scores depict that the score of MARS-OI-2 (15) is about double of MARS-OI-2 (9). So, in spite MARS-OI-1 has justifiable usefulness in the forecasting outline, MARS-OI-2 outperforms it.Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement
https://aeis.bilijipub.com/article_169078.html
Immunizing projects such as piled bridges entails considerations that ensure safety over the operation period. Pile Settlement (PS) which seems one of the most critical matters in constructional project failure, has attracted experts&rsquo; attention to be predicted before starting projects with piles. The variables to appraise the pile movement would help us determine the perspectives after and during loading. Theoretical ways to calculate the pile movement mathematically have been adopted to model the PS, mostly by using artificial intelligence (AI). This paper has aimed to estimate the pile settlement rates based on pile samples. For this reason, a new hybrid model containing a Radial Basis Function Neural Network (RBFNN) joining with Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO) were used in a framework. In fact, optimizers utilized for calculating the neuron number of hidden layer in RBFNN at optimal level. In Malaysia, the Kuala Lumpur transportation network was investigated to examine the pile movement based on ground conditions and properties through the developed hybrid RBF-GWO and RBF-ACO algorithm. Evaluating each framework&rsquo;s performance was done via the indices. So, the RMSEs of RBF-GWO and RBF-ACO reached values 0.5176 and 0.6562, respectively, and the MAE showed the rates 0.2583 and 0.3386, respectively. The correlation R-value also showed the RBF-GWO suitable accuracy with 1.23 percent higher than another model. Therefore, results have implied the RBF-GWO desirable performance to estimate PS.Estimation of the Compressive Strength of Self-compacting concrete (SCC) by a Machine Learning Technique coupling with Novel Optimization Algorithms
https://aeis.bilijipub.com/article_169079.html
Self-compacting concrete (SCC), as a liquid aggregate, is suitable for use in reinforced constructions with no need for vibration. SCC utilization has been found in a wide range of projects. Nevertheless, those applications are often limited due to lacking the knowledge about such mixed materials, especially from experimental testing. The factor of Compressive Strength (CS), which is one of the vital mechanical variables in structure immunization, can be computed either through costly tests or predictive models. Intelligent systems can appraise CS based on ingredients&rsquo; data fed to the models. This research aims to model the CS of SCC via a machine learning technique of Support Vector Regression (SVR). The Particle Swarm Optimization (PSO) and Henry&rsquo;s Gas Solubility Optimization (HGSO) have been utilized to optimize the SVR in finding some internal parameters. Different metrics were chosen to evaluate the performance of models. Consequently, the R2 in the testing stage for SVR-HGSO was computed at 0.90 and for SVR-PSO, 0.93. In the calibration phase, the correlation rate was computed at 0.93 for SVR-HGSO with a 3% difference from the SVR-PSO with 0.90.The optimal machine learning model for the precise prediction of high-performance concrete strength property
https://aeis.bilijipub.com/article_169080.html
The prominent mechanical property of concrete is compressive strength which guarantees the performance and safety of the structure in its life cycle. Assessment of compressive strength, especially for high-performance concrete, is first due to the nonlinear relationship between the compressive strength and the concrete constituents. Second, the supplementary cementitious materials admixed with the mix design of high-performance concrete encounter difficulties. The machine learning-based method, which relies on data mining, helps develop reliable and precise models to predict compressive strength. The present study employs a machine learning-based support vector regression (SVR) method to implement compressive strength prediction. The model accuracy was enhanced and strengthened by tuning the practical constraints of the support vector regression method. Marine predator and grasshopper optimization algorithms are performing the tuning process. The results of hybrid models show that the marine predator-based algorithm (MPA-SVR) played better than the grasshopper-based model (GOA-SVR) in predicting the compressive strength of high-performance concrete. The values of R2 for MPA-SVR and GOA-SVR are reported as 0.9939 and 0.9873, which implies that the MPA-SVR is more capable of implementing the compressive strength prediction.Real-time Classification and Hepatitis B Detection with Evolutionary Data Mining Approach
https://aeis.bilijipub.com/article_169081.html
Hepatitis is a disease that occurs in all ages and levels of the life of people. Hepatitis disease does not only have a deadly effect, but its identification, diagnosis, and early detection can help to treat the disease in the body and care and maintenance. Hepatitis has a variety of types that this type of study deals with hepatitis B. In this research, a new classification approach is developed for the diagnosis of hepatitis B disease using an optimized deep-learning method. This method, which involves the automatic extraction of features with minimum redundancy and minimum possible dimensions, and then modeling data from a low to a high level, can be used as a data mining method in the discovery and extraction of knowledge in computer-aided medical systems to be employed. Also, a series of evaluation criteria, including accuracy, to compare with the previous methods and to ensure the proposed approach is presented.Prediction the compaction properties of lateritic soils by hybrid ANFIS methods
https://aeis.bilijipub.com/article_169082.html
Empirically, soil compaction is an important aspect in the selection of materials for earth constructions. Due to time constraints and attention to completion resources, it is necessary to develop models to forecast compaction parameters (maximum dry unit weight (&gamma;_dmax) and optimum moisture content (&omega;_opt) from easily measured index properties. The main purpose of this study is to scrutinize the applicability of using the hybrid adaptive neuro-fuzzy inference system (ANFIS) models for predicting the &gamma;_dmax and &omega;_opt related to the standard proctor compaction test of lateritic soils. Results present that both models have a reasonable performance in predicting the &gamma;_dmax and &omega;_opt with R^2 larger than 0.9038 and 0.9692 for the training data, representing the acceptable correlation between measured and forecasted &gamma;_dmax and &omega;_opt. Regarding developed models, the ANFIS model optimized with whale optimization algorithm (WOA) has the best performance than imperialist competitive algorithm (ICA) model in both training and testing phases for predicting &gamma;_dmax and &omega;_opt.Performance improvement of a flash-binary geothermal power system using zeotropic working fluid; A comprehensive exergoeconomic analysis and optimization
https://aeis.bilijipub.com/article_169083.html
In this paper, a geothermal system is combined with an organic Rankine cycle to generate power. The zeotropic mixture is utilized to improve the organic Rankine cycle performance. The mass, energy, exergy, and exergoeconomic analysis is applied to evaluate the proposed system performance, in which the system led generated 3841 kW net power with 61.09% exergetic efficiency and 3.55 years of payback period. Then, a parametric study is performed to obtain the effect of vapor generator temperature and zeotropic mixture&rsquo;s mass fraction on the proposed system&rsquo;s main performance criteria. Based on the parametric study results, the mass fraction variation influences the net power generation, energy and exergetic efficiencies, and the payback period is higher than the evaporation temperature in the vapor generator unit while, the exergy destruction is influenced by the evaporation temperature higher than the zeotropic mixture mass fraction. Also, the net present value is estimated for three different geofluid and electricity sale prices. Increasing the electricity price about 22% with the same geofluid price decreases the payback period by about 23% and improves the system profit by about 54.7%. Finally, applying a multi-objective optimization refers to obtaining the payback and exergetic efficiency by about 3.26 years and 62.15%, respectively.Exergy, energy, economic, and environmental assessment of gas condensate stabilization units for the selection of optimum configuration
https://aeis.bilijipub.com/article_169084.html
In this paper, five structures for gas condensate stabilization are simulated and analyzed from the energy, exergy, economic and environmental points of view. These structures are simulated using Aspen HYSYS and Peng-Robinson fluid package. The studied structures are stabilizer column with reboiler and condenser and without preheating (STB-A), stabilizer column with reboiler and without condenser (STB-B), stabilizer column with reboiler, without condenser and with preheating (STB-C), stabilizer column with reboiler, condenser and preheater (STB-D), and stabilizer column with reboiler, without condenser and with side reboiler (STB-E). Exergy efficiency, total production cost, reboiler energy, and total CO2 emission are calculated for all the structures and compared. According to the performed analysis, STB-E with exergy destruction of 681.9 kW has the highest exergy efficiency (36.37%) among all the studied structures. In addition, technical assessment showed that the STB-C has the highest loss of hydrocarbons through the overhead vapors of the stabilization column. Based on the economic analysis it is deduced that the values of total production costs in STB-C, STB-D, and STB-E are 7.14% lower than the total production costs values of the structures without integration (STB-A and STB-B). Finally, it is determined that the STB-E is the only structure that can maintain the quality of RVP (8 psia) for the produced condensate while simultaneously controlling all the technical, economic and, environmental parameters at desirable levels.