Application Of Soft Computing Techniques Research Articles

Application of neural networks and neuro-fuzzy models in construction scheduling

Construction scheduling is a complex process that involves a large number of variables, making it difficult to develop accurate and efficient schedules. Traditional scheduling techniques rely on manual analysis and intuition, which are prone to errors and often fail to account for all the variables involved. This results in project delays, cost overruns, and poor project performance. Artificial intelligence models have shown promise in improving construction scheduling accuracy by incorporating historical data, site-specific conditions, and other variables that traditional scheduling methods may not consider. In this research study, application of soft-computing techniques to evaluate construction schedule and control of project activities in order to achieve optimal performance in execution of building projects were carried out. Artificial neural network and neuro-fuzzy models were developed using data extracted from a residential two-storey reinforced concrete framed-structure construction schedule and project execution documents. The evaluation of project performance indicators in earned value analysis from 0 to 100% progress at 5% increment with a total of seventeen tasks were carried out using Microsoft Project software and data obtained from the computation were utilized for model development. Using input–output and curve-fitting (nftool) function in MATLAB, a 6-10-1 two-layer feed-forward network with tansig activation-function (AF) for the hidden neurons and linear AF output neurons was generated with Levenberg–Marquardt (Trainlm) training algorithm. Similarly, with the aid of ANFIS toolbox in MATLAB software, the training, testing and validation of the ANFIS model were carried out using hybrid optimization learning algorithm at 100 epochs and the Gaussian-membership-function (gaussmf). Loss-function parameters namely MAE, RMSE and R-values were taken as the performance evaluation criteria of the developed models. The generated statistical results indicates no significant difference between model-results and experimental values with MAE, RMSE, R2 of 1.9815, 2.256 and 99.9% respectively for ANFIS-model and MAE, RMSE, R2 of 2.146, 2.4095 and 99.998% respectively for the ANN-model. The model performance indicated that the ANFIS-model outclassed the ANN-model with their results satisfactory to deal with complex relationships between the model variables to produce accurate target response. The findings from this research study will improve the accuracy of construction scheduling, resulting in improved project performance and reduced costs.

Application of Soft Computing Techniques in River Flow Modeling

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority.

Application of Soft Computing Techniques in Modelling of Soaked and Unsoaked California Bearing Ratio

In this study, author attempted to establish a correlation between soil physical parameters and California Bearing Ratio of lateritic soils using advanced mathematical techniques such as the Support Vector Machine (SVM), Random Forest (RF), M5 tree, multiple linear regression, and Artificial Neural Network. A total of 480 soil samples were collected and separated into a data set using training and validation of the generated models based on the main soil parameters of Liquid Limit (LL), Plastic Limit (PL), Natural moisture content (NMC), Specific gravity (GS), Fines (F), Gravel, and Sand. The Principal Component Analysis (PCA) was used to minimize the dataset's huge dimension, and the approximate sum of the first four principal components (PC) captured 88 percent of the variability in the response variable with just 12% information loss. The RMSE values of 21.6, 21.23, 295.67, 7.03, 14.54 and 24.43,24.59,326.49,8.63,17.71 are from the MLR, ANN, MS Tree, RF, and SVM models for SCBR and USCBR values, respectively. For SCBR and USCBR, random forest (RF) yielded the lowest values of 7.03 and 8.63, respectively. Similarly, the R values range from 0.1 to 0.94 and 0.01 to 0.92, indicating that the anticipated and real SCBR and USCBR are related. The Random Forest Model for SCBR and USCBR was shown to be the best by the correlation coefficient values, while the MS tree model for SCBR and USCBR was determined to have the lowest coefficient of determination R2. As a result, it can be concluded that Random Forest provided the best Soaked and Unsoaked CBR model based on the dataset, while MS tree provided the poorest model. The model is a valuable tool for evaluating the subsurface indices of a civil engineering site at the preliminary planning stage before final structural design for the substructures, as the anticipated soil parameter values are within permitted accuracy.

Application of Soft Computing Techniques for Porosity Optimization of Dye Sensitized Solar Cell

ABSTRACT In this paper, the evolutionary computation-based techniques have been introduced for porosity optimization of dye-sensitized solar cell (DSSC) with comparative analysis. The diffusion differential equation-based model of DSSC achieves the goal. The porosity has been considered for optimization as it influences the light absorption and electron diffusion rate. Due to that reason, the cell performance differs at different porosities. This parameter with proper tuning can help to extract the maximum efficiency irrespective of environmental factors. The search and optimization tools, such as artificial bee colony, differential evolution, genetic algorithm, particle swarm optimization, and simulated annealing (SA), is used and applied to the DSSC model for the optimization. The classic optimization algorithms have been compared, and an investigation has been carried out at different thickness values of the titanium dioxide () layer. This study results the realization of the best approach in terms of convergence and computational time, and the consistency of the optimized porosity has been examined at distinct porosity. It is convenient for the practical model improvement of DSSCs with better efficiency.

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

A Short Overview of Soft Computing Techniques in Tunnel Construction

Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon.

A Short Overview of Soft Computing Techniques in Tunnel Construction

Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon.

Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

This research presents optimal factor evaluation for maximum Dyacrodes edulis seed oil (DESO) extraction by applying central composite design (CCD) based on Box-Behnken (BB) experimental design of response surface methodology (RSM) and Artificial neural network (ANN) on feed forward-back propagation (FFBP) of Levenberg Marquardt (LM) training algorithm. Polar solvents (ethanol and combination of methanol and chloroform (M/C)) and non-polar solvents (n-hexane) were used for the extraction. The RSM optimal predicted oil yields were 45.21%, 38.61% and 30.87% while experimental values were 46.01%, 40.71% and 32.45% for n-hexane, ethanol and M/C respectively. The RSM optimum conditions were particle size of 450.67, 451.19 and 450.22μm, extraction time of 55.57, 55.16 and 56.11min and solute/solvent ratio of 0.19, 0.16 and 0.18 g/ml for n-hexane, ethanol and M/C respectively. The ANN-GA optimized conditions showed 5.14, 5.81 and 2.12 % higher DESO yields at 1.10, 0.26 and 0.65% smaller particle sizes, 5.47, 0.30 and 0.62 % faster extraction rate, and 24, 11.11 and 10% more solute requirement, for n-hexane, ethanol and M/C solvents respectively. The particle size was found to be the most significant factor. ANN and RSM established good correlations with the experimental data but ANN showed higher predictive supremacy than RSM based on its higher values of R2 and lower error indices. Also, ANN-GA provided more economical optimal DESO extraction route. The physico-chemical characteristics, functional groups and fatty acid compositions of the seed oil compared with literature values and suggest high commercial values for DESO. Therefore, the obtained results present a viable method to harness the useful and highly potential seed oil from dyacrodes edulis for many industrial applications.

Application of soft-computing techniques in forecasting sediment load and concentration

ABSTRACT This paper investigates conventional and soft-computing methods for the estimation of suspended sediment concentration (SSC) and load (SSL) in rivers. Frequently used methods of sediment rate curve (SRC) and multi-nonlinear regression, and soft-computing methods of multi-layer perceptron, multi-linear regression and adaptive neuro-fuzzy inference system are implemented using various hydrological and hydraulic parameters for the Little Kickapoo Creek Watershed, Illinois, USA. All methods performed equally well in the estimation of SSL, without any noticeable outperformance from any from the methods. However, the application of soft-computing methods decreased SSC estimation errors considerably as compared to the results of SRC. The results are significant in the way they reconcile traditionally used hydrological parameters into the soft-computing methods. Overall, soft-computing methods are recommended for the estimation of SSC in rivers because of their reasonably better performance and ease of implementation.

Análisis del estado de arte de Técnicas de Soft Computing aplicadas a problemas de planificación de red en 5G.

En el presente artículo se realizó una revisión del estado del arte de la aplicación de las técnicas de Soft Computing en la resolución de problemas de planificación de redes 5G, para lo cual se clasificó las diferentes técnicas de soft computing existentes (redes neuronales, lógica difusa, algoritmos evolutivos) y de los trabajos e investigaciones realizados sobre el tema según sus autores, los modelos planteados y los métodos de solución. Adicionalmente se describieron las investigaciones más relevantes en donde se especifican técnicas para dar solución a los problemas de arquitecturas y funcionalidades cruciales en el desarrollo de esta tecnología, entre los cuales se resalta: encontrar una posición óptima para una Estación Base (BS) en un área de interés determinada, operar en las bandas de frecuencias múltiples deseadas mientras se mantiene una alta ganancia, limitar el consumo de energía en las infraestructuras de red 5G y tratar de incrementar la calidad del servicio al disminuir la probabilidad de bloqueo de llamadas. Finalmente se concluyó que las técnicas de soft computing más aplicadas a la solución de problemas de planificación de las redes 5G son lógica difusa, para limitar el consumo de energía en las infraestructuras de red 5G, además de las redes neuronales artificiales y algoritmos genéticos para la admisión de llamadas en redes 5G con la finalidad de incrementar la calidad del servicio al disminuir las interferencias.

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence.

Applications of Soft Computing Techniques for Pulmonary Tuberculosis Diagnosis

Recently, several interesting research studies have been reported on soft computing approaches. Soft computing approaches are solving several kinds of problems and provide alternative solutions. Different Soft computing techniques or approaches have been applied in medical care data for effective diagnosis prediction. Those approaches implemented on diseases diagnosing of pulmonary tuberculosis and obtaining better results in comparison to traditional approaches. This approach is an aggregation of methodologies that were combined various model and provide solutions to those problems that are difficult to handle in real-world situations. Researchers keep developing of an accurate and reliable intelligent decision-making method for the construction of pulmonary tuberculosis diagnosis system. The existing diagnostic testing system procedures are not only tedious, they also take a long time to analyze. Therefore, the diagnosis of tuberculosis still requires further improvements to new rapid and accurate diagnostic model and techniques that enable higher sensitivity and specificity to be achieved, thus promoting disease control and Prevention. State of the art makes approaches to soft computing more powerful, more reliable and more efficient. The importance of this review paper is to distinguish the different soft computing approaches used to support pulmonary tuberculosis disease diagnosis, identification, prediction and intelligent classification. In the field, researchers and medical practitioners look forward to using approaches to soft computing. Some of these are an artificial neural network, genetic algorithm, and support vector machine, fuzzy logic etc. latest methods in the diagnostic field uses artificial neural network. Some of the other benefits of Artificial neural network is an easy - to - optimize, resources and adoptable non - linear modeling of expansive data sets and predictive inference accuracy demonstrating that artificial neural network could serve as a valuable decision support tool in various fields, including medicine

Application of Soft Computing Techniques for River Flow Prediction in the Downstream Catchment of Mahanadi River Basin Using Partial Duration Series, India

The delta region of the Mahanadi river basin is faced with high floods having arisen many questions like (1) due to the release of Hirakud dam, (2) due to the middle region catchment contribution (Tel tributary at Kantamal station and Ong tributary at Salebhata station) and (3) due to an increase in heavy rainfalls over the basin. To answer this, we choose the partial duration series (PDS) having an average number of flood peaks per year (λ = 3) at Naraj based on the capacity of the existing embankment which mitigates floods up to 28,400 m3/s and analyze the trends of peak discharge of PDS having λ = 3 at the Naraj, Hirakud dam and middle region to know the flow behavior which is responsible for flooding problem in the delta of Mahanadi basin. Subsequently, the trend of extreme rainfall over the entire basin and at the middle region of the basin is analyzed. Analysis of trends of extreme floods in relation to the trends of extreme rainfall to have a proper understanding of the causes of changing floods over the basin is important. Then, runoff at Naraj is estimated based on two techniques: artificial neural networks with different algorithms and an adaptive neuro-fuzzy inference system (ANFIS) using five different input combinations of peak runoff data and rainfall data of upstream locations and compare their performance based on different indices, namely correlation coefficient (R), index of agreement (d), root-mean-square error and Nash–Sutcliffe efficiency (E) to obtain the best input–output model. From the analysis, it is shown that ANFIS techniques as well as the neural network with Levenberg–Marquardt algorithm performs better for estimating runoff at Naraj and that gives the clear idea about the delta flooding. Also, the analysis revealed the answer arisen by many questions regarding the delta flood problem and which are due to the middle region catchment contribution and an increase in extreme rainfall.

A Review on Application of Soft Computing Techniques in Machining of Particle Reinforcement Metal Matrix Composites

In this paper, a wide literature review of soft computing methods in conventional machining processes of metal matrix composites is carried out. The tool wear, cutting force along with surface quality are presented in the different types of machining processes and examined thoroughly. Summary of the different particular soft computing approaches in machining such as turning, milling, drilling and grinding operations are thoroughly discussed. Furthermore, this work put emphases on the optimization and modeling of the machining process. The study will emphasis on the most general methods used by researchers in literature for developing the statistical and mathematical modeling using soft computing approaches including, genetic algorithm, response surface methodology, fuzzy logic, artificial neural network, Taguchi method and particle swarm optimization. In last section the comprehensive open issues and conclusion are presented for application of soft computing techniques in machining of metal matrix composite performance prediction and optimization.

A Review on Application of Soft Computing Techniques for Load Shedding in Power Systems

In power systems, system frequency decreases if load exceeds generation and increases when power generation is greater than load demand. Load shedding is highly required in power systems to stabilize frequency in electrical power systems. Though conventional load shedding techniques are being used in many power system applications, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic, genetic algorithm and particle swarm optimization have been presented by many researchers to provide optimum load shedding. However, there are many problems associated with machine learning based control techniques in real time. In this work, the utilization of soft computing algorithms and its benefits and drawbacks are discussed. A comprehensive survey is presented about the soft computing based load shedding and comparison is made among these techniques and conventional techniques. Neural network based learning rule is applied with back propagation network to minimize the error. In fuzzy based technique, load shedding position at each node was predetermined by applying certain fuzzy rules. Genetic algorithm and particle swarm optimization are robust and applied to solve many nonlinear and multi-objective problems. ANN and fuzzy logic are combined to build an adaptive neuro fuzzy inference system to provide accurate load shedding.

Application of Soft Computing Techniques in Global Software Development: state-of-the-art Review

Developing Software through a globally distributed team is a modern trend, which is not only cost effective but also yields best project results mitigating risk and increasing return on investment. This is easily achieved by ensuring through put in production is maintained at all times irrespective of the clock time and geographical boundaries. This shift of phenomenon is happening across the board as more and more companies use this as a strategic tool. Modern day technology makes this all possible, without compromising quality, coding practices and project management techniques. In this paper we have researched several papers (2008 to 2018) and understood the data for soft computing to provide a strong basis for future directions Â

A SOFT COMPUTING TECHNIQUES AND THEIR APPLICATIONS

Exponential growth in soft computing technologies has marked new milestones in powerful representation, modelling paradigms and optimization mechanisms for solving real time issues. With the great expansion of for computing area, a substantial amount of research efforts has been directed at the application of Soft Computing techniques in engineering. Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. The techniques of soft computing are nowadays being used successfully in many domestic, commercial, and industrial applications. This paper we gives an overview of the state of soft computing techniques and describes the various application to produce specification according need of current generation.

Big data: the key to energy efficiency in smart buildings

Due to the high impact that energy consumption by buildings has at global scale, energy-efficient buildings to reduce $$\mathrm{CO}_2$$CO2 emissions and energy consumption are needed. In this work we present a novel approach to energy saving in buildings through the identification of the relevant parameters and the application of Soft Computing techniques to generate predictive models of energy consumption in buildings. Using such models it is possible to define strategies for optimizing the day-to-day energy consumption of buildings. To verify the feasibility of this proposal, we apply our approach to a reference building for which we have contextual data from a complete year of monitoring. First, we characterize the building in terms of its contextual features and energy consumption, and then select the most appropriate techniques to generate the most accurate model of our reference building charged with estimating the energy consumption, given a concrete set of inputs. Finally, considering the energy usage profile of the building, we propose specific control actions and strategies to save energy.

Application of soft computing techniques for multi source deregulated power system

In this paper, an interconnected power system is proposed for Automatic Generation Control (AGC) in restructured power environment. The customized AGC scheme is projected in deregulated environment for multi-source combination of hydro, reheat thermal and gas generating units in entire area. Proportional integral derivative controller is offered for AGC scheme and the gains are optimised through soft computing techniques such as Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm, Real Coded Genetic Algorithm (RCGA) and also with Artificial Neural Network (ANN). The PSO chosen here carves out the AGC problem through the addition of adaptive inertia weight factor and adaptive constriction factors. The intense trend in deregulated system leads to the aggressiveness in frequency and tie line power deviations. It is observed that the chaos mapping of PSO enhance the rate of convergence using logistics map sequence. The proposed algorithms are tested on three area power system for different electricity contracted scenarios under various operating conditions with Generation Rate Constraint (GRC). Analysis reveals that proposed HCPSO improves significantly the dynamical performances of system such as settling time and overshoot. The comparative results show the robust performance of HCPSO against parametric uncertainties for a wide range of load demands and disturbances. Key words: Automatic generation control (AGC), hybrid chaotic particle swarm optimisation (HCPSO), proportional integral derivative (PID), restructured power system.