Special Issue "Soft Computing Techniques in Structural Engineering and Materials"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editor

Prof. Dr. Panagiotis G. Asteris
Website
Guest Editor
Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece
Interests: applied mathematics and numerical methods; computational mechanics; artificial neural networks; masonry structures
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Special Issue Information

Dear Colleagues,

During the last three decades, nonconventional methods have become an important class of efficient tools, providing solutions to complicated engineering problems. Among these methods, soft computing has to be mentioned as one of the most eminent approaches. Neural networks (NNs), fuzzy logic, and evolutionary algorithms are the most popular soft-computing techniques.

The focus of this Special Issue is on nondeterministic computational methods for the modeling of structural engineering and materials problems. Articles submitted to this Special Issue can also be concerned with the most significant recent developments in computational methods and their applications in structural engineering and materials problems. We invite researchers to contribute original research articles, as well as review articles, that will stimulate the continuing research effort on applications of the soft computing approaches to model structural engineering and materials problems.

Prof. Dr. Panagiotis G. Asteris
Guest Editor

Manuscript Submission Information

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Keywords

  • Methodologies
  • State-of-the-art on a specific theme
  • Genetic, evolutionary computation
  • Swarm intelligence
  • Neural networks, support vector machines
  • Fuzzy logic and fuzzy systems
  • Hybrid algorithms
  • Topology optimization
  • Fragility analysis
  • Structural design, diagnostics, and health monitoring
  • Modeling of mechanical properties of structural materials
  • Comparison and Validation of Artificial Neural Networks with other Machine Learning models

Published Papers (34 papers)

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Research

Open AccessArticle
Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
Appl. Sci. 2020, 10(6), 2108; https://doi.org/10.3390/app10062108 - 20 Mar 2020
Abstract
In order to improve the manufacturing quality of holes (Φ3–Φ8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back [...] Read more.
In order to improve the manufacturing quality of holes (Φ3–Φ8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back propagation (BP) neural network algorithm. Four parameters of feed rate, spindle speed, drilling diameter, and cushion plate were taken as the input layer parameters to study the manufacturing quality of holes in four stack types: CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP. Delamination and tearing defects often appear in the drilling process; thus, a certain degree of defects in CFRP was selected as the output parameter, in an effort to build a prediction model of drilling quality. After the neural network model of the optimized hole-making process of an 8–14–1 three-layer topology was corrected by 170 steps, the error was reduced to 0.00016882, the regression fitting was 0.99978, and the fitting error of training samples was 10−2~10−5. The prediction model of the number of defective holes provided basically similar results to the experimental data. This indicates that the prediction model based on a BP neural network has good prediction ability. Based on the prediction of parameters, verification tests were performed, and the number of defective holes in CFRP was reduced while the manufacturing quality of the holes was improved significantly; the qualified rate of manufactured holes reached 97%. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
On the Use of Neuro-Swarm System to Forecast the Pile Settlement
Appl. Sci. 2020, 10(6), 1904; https://doi.org/10.3390/app10061904 - 11 Mar 2020
Cited by 1
Abstract
In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of [...] Read more.
In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
Appl. Sci. 2020, 10(5), 1871; https://doi.org/10.3390/app10051871 - 09 Mar 2020
Abstract
Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 [...] Read more.
Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network
Appl. Sci. 2020, 10(3), 1185; https://doi.org/10.3390/app10031185 - 10 Feb 2020
Abstract
As a lateral load-bearing system, the steel plate shear wall (SPSW) is utilized in different structural systems that are susceptible to seismic risk and because of functional reasons SPSWs may need openings. In this research, the effects of rectangular openings on the lateral [...] Read more.
As a lateral load-bearing system, the steel plate shear wall (SPSW) is utilized in different structural systems that are susceptible to seismic risk and because of functional reasons SPSWs may need openings. In this research, the effects of rectangular openings on the lateral load-bearing behavior of the steel shear walls by the finite element method (FEM) is investigated. The results of the FEM are used for the prediction of SPSW behavior using the artificial neural network (ANN). The radial basis function (RBF) network is used to model the effects of the rectangular opening in the SPSW with different plate thicknesses. The results showed that the opening leads to reduced load-bearing capacity, stiffness and absorbed energy, which can be precisely predicted by employing RBF network model. Besides, the suitable relative area of the opening is determined. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
GIS and BIM as Integrated Digital Environments for Modeling and Monitoring of Historic Buildings
Appl. Sci. 2020, 10(3), 1078; https://doi.org/10.3390/app10031078 - 06 Feb 2020
Cited by 1
Abstract
Multidisciplinary data integration within an information system is considered a key point for rehabilitation projects. Information regarding the state of preservation and/or decision making, for sustainable restoration is prerequisite. In addition, achieving structural integrity of a historic building, especially one that has undergone [...] Read more.
Multidisciplinary data integration within an information system is considered a key point for rehabilitation projects. Information regarding the state of preservation and/or decision making, for sustainable restoration is prerequisite. In addition, achieving structural integrity of a historic building, especially one that has undergone many construction phases and restoration interventions, is a very elaborate task and should, therefore, involve the study of multidisciplinary information regarding historical, architectural, building material and geometric data. In this paper the elaboration of such data within 2D and 3D information systems is described. Through the process described herein, a methodology, including the acquisition, classification and management of various multisensory data, is displayed and applied within a geographic information system (GIS). Moreover, the multidisciplinary documentation process, aggregated with the surveying products, generates 3D heritage building information modeling (HBIM), including information regarding construction phases, pathology and current state of preservation of a building. The assessment of the applied methodology is performed concluding in a qualitative and a quantitative manner, in both 2D and 3D environments, providing information to facilitate the structural assessment of a historic building. Thus, in this work, the described methodology is presented, combining the multidisciplinary data with the development of GIS thematic maps and an HBIM. Representative results of the suggested methodology applied on the historic building of Villa Klonaridi, Athens, Greece are displayed. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Bionic Stiffener Layout Optimization with a Flexible Plate in Solar-Powered UAV Surface Structure Design
Appl. Sci. 2019, 9(23), 5196; https://doi.org/10.3390/app9235196 - 29 Nov 2019
Cited by 2
Abstract
A cellular-based evolutionary topology optimization scheme over a small curvature big contour wing surface is proposed for the design of an ultralight surface structure. Using this method, a ground-structure technique is first applied to obtain homogeneous mesh generation with a predefined weight value [...] Read more.
A cellular-based evolutionary topology optimization scheme over a small curvature big contour wing surface is proposed for the design of an ultralight surface structure. Using this method, a ground-structure technique is first applied to obtain homogeneous mesh generation with a predefined weight value over the design domain. Secondly, the stiffener path’s description is guided by a modified map L system topology method that simulates the growth of the bionic branch, and the structural components are obtained by the specified searching method according to weights of the previous mesh vertexes. Thirdly, an optimal curved stiffener layout is achieved using an agent-based algorithm to create individual instances of designs based on a small number of input parameters. These parameters can then be controlled by a genetic algorithm to optimize the final design according to goals like minimizing weight and structural weakness. A comparison is implemented for long-span panel stiffener layout generation between an initial straight case and a bionic optimal case via our method, thereby indicating the significant improvement of the buckling loads by steering the stiffener’s path. Finally, this bionic method is applied to the wing box structure design and achieves remarkable weight loss at last. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt
Appl. Sci. 2019, 9(15), 3172; https://doi.org/10.3390/app9153172 - 04 Aug 2019
Cited by 15
Abstract
The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability [...] Read more.
The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining
Appl. Sci. 2019, 9(14), 2835; https://doi.org/10.3390/app9142835 - 16 Jul 2019
Abstract
Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by [...] Read more.
Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by the complexity of cases, the quality of the dataset, and the sufficiency of variables. In the present study, the impact of variation/response space complexity and variable completeness on backpropagation (BP) ANN model establishment was investigated, with a steel ladle lining from secondary steel metallurgy as the case study. The variation dataset for analysis comprised 160 lining configurations of ten variables. Thermal and thermomechanical responses were obtained via finite element (FE) modeling with elastic material behavior. Guidelines were proposed to define node numbers in the hidden layer for each response as a function of the node number in the input layer weighted with the percent value of the significant variables contributing above 90% to the response, as well as the node number in the output layer. The minimum numbers of input variables required to achieve acceptable prediction performance were three, five, and six for the maximum compressive stress, the end temperature, and the maximum tensile stress. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm
Appl. Sci. 2019, 9(14), 2802; https://doi.org/10.3390/app9142802 - 12 Jul 2019
Cited by 5
Abstract
It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties [...] Read more.
It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks
Appl. Sci. 2019, 9(14), 2788; https://doi.org/10.3390/app9142788 - 11 Jul 2019
Cited by 28
Abstract
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk [...] Read more.
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm
Appl. Sci. 2019, 9(13), 2589; https://doi.org/10.3390/app9132589 - 26 Jun 2019
Cited by 3
Abstract
In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment [...] Read more.
In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
Appl. Sci. 2019, 9(12), 2562; https://doi.org/10.3390/app9122562 - 23 Jun 2019
Cited by 6
Abstract
There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the [...] Read more.
There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections
Appl. Sci. 2019, 9(11), 2258; https://doi.org/10.3390/app9112258 - 31 May 2019
Cited by 20
Abstract
The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling [...] Read more.
The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderness ratios and initial geometric imperfections, were considered. The predicted target was the critical buckling load of columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE) were used to evaluate and validate the three proposed AI models. The results showed that SA and BBO were able to improve the prediction performance of the original ANFIS. Excellent results using the BBO optimization technique were achieved (i.e., an increase in R by 7.15%, RMSE by 40.48%, and MAE by 38.45%), and those using the SA technique were not much different (i.e., an increase in R by 5.03%, RMSE by 26.68%, and MAE by 20.40%). Finally, sensitivity analysis was performed, and the most important imperfections affecting column buckling capacity was found to be the initial in-plane loading eccentricity at the top and bottom ends of the columns. The methodology and the developed AI models herein could pave the way to establishing an advanced approach to forecasting damages of columns under compression. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
A Sequential Approach to Numerical Simulations of Solidification with Domain and Time Decomposition
Appl. Sci. 2019, 9(10), 1972; https://doi.org/10.3390/app9101972 - 14 May 2019
Cited by 6
Abstract
Progress in computational methods has been stimulated by the widespread availability of cheap computational power leading to the improved precision and efficiency of simulation software. Simulation tools become indispensable tools for engineers who are interested in attacking increasingly larger problems or are interested [...] Read more.
Progress in computational methods has been stimulated by the widespread availability of cheap computational power leading to the improved precision and efficiency of simulation software. Simulation tools become indispensable tools for engineers who are interested in attacking increasingly larger problems or are interested in searching larger phase space of process and system variables to find the optimal design. In this paper, we show and introduce a new approach to a computational method that involves mixed time stepping scheme and allows to decrease computational cost. Implementation of our algorithm does not require a parallel computing environment. Our strategy splits domains of a dynamically changing physical phenomena and allows to adjust the numerical model to various sub-domains. We are the first (to our best knowledge) to show that it is possible to use a mixed time partitioning method with various combination of schemes during binary alloys solidification. In particular, we use a fixed time step in one domain, and look for much larger time steps in other domains, while maintaining high accuracy. Our method is independent of a number of domains considered, comparing to traditional methods where only two domains were considered. Mixed time partitioning methods are of high importance here, because of natural separation of domain types. Typically all important physical phenomena occur in the casting and are of high computational cost, while in the mold domains less dynamic processes are observed and consequently larger time step can be chosen. Finally, we performed series of numerical experiments and demonstrate that our approach allows reducing computational time by more than three times without losing the significant precision of results and without parallel computing. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
Appl. Sci. 2019, 9(9), 1845; https://doi.org/10.3390/app9091845 - 05 May 2019
Cited by 2
Abstract
Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the parameters often requires the modeling of high-dimensional functions with [...] Read more.
Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the parameters often requires the modeling of high-dimensional functions with limited information, and it is challenging to choose an effective surrogate model over the unknown design space. To this end, the ensemble models—combined with different surrogate models—offer effective solutions. This paper presents a new ensemble model based on the least squares method, which is a regularization strategy and an augmentation strategy; we call the model the regularized least squares ensemble model (RLS-EM). Three individual surrogate models—Kriging, radial basis function, and support vector regression—are used to compose the RLS-EM. Further, the weight factors are estimated by the least squares method without using the global or local error metrics, which are used in most existing methods. To solve the collinearity in the least squares calculation process, a regularization strategy and an augmentation strategy are developed. The two strategies help explore the unknown regions and improve the accuracy on one hand; on the other hand, the collinearity can be reduced, and the overfitting phenomenon that may occur can be avoided. Six numerical functions, from two-dimensional to 12-dimensional, and a computer numerical control (CNC) milling machine bed design problem are used to verify the proposed method. The results of the numerical examples show that RLS-EM saves a considerable amount of computation time while ensuring the same level of robustness and accuracy compared with other ensemble models. The RLS-EM used for the CNC milling machine bed design problem also shows good accuracy characteristics compared with other ensemble methods. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
Appl. Sci. 2019, 9(8), 1621; https://doi.org/10.3390/app9081621 - 18 Apr 2019
Cited by 24
Abstract
The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the [...] Read more.
The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus, and confining (normal) stress. For this purpose, case studies of 165 rockfill samples have been applied to generate training and testing datasets to construct and validate the models. Thirteen key material properties for rockfill characterization were selected to develop the proposed models. Validation and comparison of the models have been performed using the root mean square error (RMSE), coefficient of determination (R2), and mean estimation error (MAE) between the measured and estimated values. A sensitivity analysis was also conducted to ascertain the importance of various inputs in the prediction of the output. The results demonstrated that the Cubist model has the highest prediction performance with (RMSE = 0.0959, R2 = 0.9697 and MAE = 0.0671), followed by the random forests model with (RMSE = 0.1133, R2 = 0.9548 and MAE= 0.0665), the artificial neural network (ANN) model with (RMSE = 0.1320, R2 = 0.9386 and MAE = 0.0841), and the conventional multiple linear regression technique with (RMSE = 0.1361, R2 = 0.9345 and MAE = 0.0888). The results indicated that the Cubist and random forests models are able to generate better predictive results of the shear strength of RFM than ANN and conventional regression models. The Cubist model was considered to be more promising for interpreting the complex relationships between the influential properties of RFM and the shear strengths of RFM to some extent, which can be extremely helpful in estimating the shear strength of rockfill materials. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches
Appl. Sci. 2019, 9(6), 1113; https://doi.org/10.3390/app9061113 - 16 Mar 2019
Cited by 27
Abstract
Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to [...] Read more.
Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessFeature PaperArticle
Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models
Appl. Sci. 2019, 9(6), 1042; https://doi.org/10.3390/app9061042 - 13 Mar 2019
Cited by 49
Abstract
The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as [...] Read more.
The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessFeature PaperArticle
Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects
Appl. Sci. 2019, 9(2), 243; https://doi.org/10.3390/app9020243 - 10 Jan 2019
Cited by 32
Abstract
A methodology aiming to predict the vulnerability of masonry structures under seismic action is presented herein. Masonry structures, among which many are cultural heritage assets, present high vulnerability under earthquake. Reliable simulations of their response to seismic stresses are exceedingly difficult because of [...] Read more.
A methodology aiming to predict the vulnerability of masonry structures under seismic action is presented herein. Masonry structures, among which many are cultural heritage assets, present high vulnerability under earthquake. Reliable simulations of their response to seismic stresses are exceedingly difficult because of the complexity of the structural system and the anisotropic and brittle behavior of the masonry materials. Furthermore, the majority of the parameters involved in the problem such as the masonry material mechanical characteristics and earthquake loading characteristics have a stochastic-probabilistic nature. Within this framework, a detailed analytical methodological approach for assessing the seismic vulnerability of masonry historical and monumental structures is presented, taking into account the probabilistic nature of the input parameters by means of analytically determining fragility curves. The emerged methodology is presented in detail through application on theoretical and built cultural heritage real masonry structures. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Effect of Shear Connector Layout on the Behavior of Steel-Concrete Composite Beams with Interface Slip
Appl. Sci. 2019, 9(1), 207; https://doi.org/10.3390/app9010207 - 08 Jan 2019
Cited by 4
Abstract
In a steel-concrete composite beam (hereafter referred to as a composite beam), partial interaction between the concrete slab and the steel beam results in an appreciable increase in the beam deflections relative to full interaction behavior. Moreover, the distribution type of the shear [...] Read more.
In a steel-concrete composite beam (hereafter referred to as a composite beam), partial interaction between the concrete slab and the steel beam results in an appreciable increase in the beam deflections relative to full interaction behavior. Moreover, the distribution type of the shear connectors has a great impact on the degree of the composite action between the two components of the beam. To reveal the effect of shear connector layout in the performance of composite beams, on the basis of a developed one-dimensional composite beam element validated by the closed-form precision solutions and experimental results, this paper optimizes the layout of shear connectors in composite beams with partial interaction by adopting a stepwise uniform distribution of shear connectors to approximate the triangular distribution of the shear connector density without increasing the total number of shear connectors. Based on a comparison of all the different types of stepped rectangles distribution, this paper finally suggests the 3-stepped rectangles distribution of shear connectors as a reasonable and applicable optimal method. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Inductive Design Exploration Method with Active Learning for Complex Design Problems
Appl. Sci. 2018, 8(12), 2418; https://doi.org/10.3390/app8122418 - 28 Nov 2018
Abstract
The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect [...] Read more.
The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect of uncertainty that leads to performance variability. However, the existing design method suffers from high computational costs for pre-defined sample data, which sacrifices the accuracy of solution spaces. In this study, we present an improved implementation of the inductive design exploration method by applying the active learning algorithm that is mainly used in machine learning techniques. The purpose of this study is to minimize the sampling effort while maintaining reasonable accuracy in the exploration of design spaces, thereby alleviating computational burden. The capabilities of the improved method are highlighted and demonstrated via a design problem of the blast resistant sandwich panel. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Seismic Behaviors of Concrete Beams Reinforced with Steel-FRP Composite Bars under Quasi-Static Loading
Appl. Sci. 2018, 8(10), 1913; https://doi.org/10.3390/app8101913 - 14 Oct 2018
Cited by 3
Abstract
Steel-fiber reinforced polymer (FRP) composite bar (SFCB) is a new composite material with good corrosion resistance and designable post-yield stiffness. Substitution of steel bar with SFCB can greatly increase the durability and ultimate capacity associated with seismic performance. First, the method and main [...] Read more.
Steel-fiber reinforced polymer (FRP) composite bar (SFCB) is a new composite material with good corrosion resistance and designable post-yield stiffness. Substitution of steel bar with SFCB can greatly increase the durability and ultimate capacity associated with seismic performance. First, the method and main results of the experiment are briefly introduced, then a simplified constitutive model of composite bar material was applied to simulate the seismic behaviors of the concrete beams reinforced with SFCBs by fiber element modeling. The simulation results were found to be in good agreement with test results, indicating that the finite element model is reasonable and accurate in simulating the seismic behaviors of beams reinforced with SFCB. Based on the numerical simulation method, a parametric study was then conducted. The main variable parameters were the FRP type in composite bars (i.e., basalt, carbon FRP and E-glass FRP), the concrete strength, basalt FRP (BFRP) content in SFCBs and shear span ratio. Seismic behaviors such as load-displacement pushover curves, seismic ultimate capacity and its corresponding drift ratio of the SFCBs reinforced concrete beams were also evaluated. The results showed that (1) the fiber type of the composite bar had a great impact on the mechanical properties of the beam, among which the beam reinforced with BFRP composite bar has higher seismic ultimate capacity and better ductility. With the increase of the fiber bundle in the composite bar, the post-yield stiffness and ultimate capacity of the component increase and the ductility is better; (2) at the pre-yield stage, concrete strength has little influence on the seismic performance of concrete beams while after yielding, the seismic ultimate capacity and post-yielding stiffness of specimens increased slowly with the increase in concrete strength, however, the ductility was reduced accordingly; (3) as the shear span ratio of beams increased from 3.5 to 5.5, the seismic ultimate capacity decreased gradually while the ultimate drift ratio increased by more than 50%. Through judicious setting of the fiber content and shear span ratio of the composite bar reinforced concrete beam, concrete beams reinforced with composite bars can have good ductility while maintaining high seismic ultimate capacity. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
A Simplified Approach to Identify Sectional Deformation Modes of Thin-Walled Beams with Prismatic Cross-Sections
Appl. Sci. 2018, 8(10), 1847; https://doi.org/10.3390/app8101847 - 09 Oct 2018
Cited by 3
Abstract
In this paper, a simplified approach to identify sectional deformation modes of prismatic cross-sections is presented and utilized in the establishment of a higher-order beam model for the dynamic analyses of thin-walled structures. The model considers the displacement field through a linear superposition [...] Read more.
In this paper, a simplified approach to identify sectional deformation modes of prismatic cross-sections is presented and utilized in the establishment of a higher-order beam model for the dynamic analyses of thin-walled structures. The model considers the displacement field through a linear superposition of a set of basis functions whose amplitudes vary along the beam axis. These basis functions, which describe basis deformation modes, are approximated from nodal displacements on the discretized cross-section midline, with interpolation polynomials. Their amplitudes acting in the object vibration shapes are extracted through a modal analysis. A procedure similar to combining like terms is then implemented to superpose basis deformation modes, with equal or opposite amplitude, to produce primary deformation modes. The final set of the sectional deformation modes are assembled with primary deformation modes, excluding the ones constituting conventional modes. The derived sectional deformation modes, hierarchically organized and physically meaningful, are used to update the basis functions in the higher-order beam model. Numerical examples have also been presented and the comparison with ANSYS shell model showed its accuracy, efficiency, and applicability in reproducing three-dimensional behaviors of thin-walled structures. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
The Simulation of an Automotive Air Spring Suspension Using a Pseudo-Dynamic Procedure
Appl. Sci. 2018, 8(7), 1049; https://doi.org/10.3390/app8071049 - 27 Jun 2018
Abstract
This paper describes a numerical solution to characterize the deformation of a bellows-type air spring for automotive suspensions. In a first step, the shell structure is modeled as a practically inextensible membrane that has virtually no bending stiffness; the structure has only a [...] Read more.
This paper describes a numerical solution to characterize the deformation of a bellows-type air spring for automotive suspensions. In a first step, the shell structure is modeled as a practically inextensible membrane that has virtually no bending stiffness; the structure has only a pneumatic-elastic deformation due to the compressibility of the pressurized air. In a second step, a finite element modeling of the device using a commercial code is carried out in order to validate the first model. Complementing this work, an experimental procedure based on a pseudo-dynamic technique was implemented to simulate the behavior of the pneumatic suspension bellows subjected to dynamic loads. The method consists of a combined numeric/experimental procedure simulating a suddenly applied load. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Research on the Principle of a New Flexible Screw Conveyor and Its Power Consumption
Appl. Sci. 2018, 8(7), 1038; https://doi.org/10.3390/app8071038 - 26 Jun 2018
Cited by 6
Abstract
A new screw conveyor with flexible discrete spiral blades is proposed to solve the problem of particle material gathering between the screw and the tube wall in the traditional screw conveyor. With a theoretical analysis, the power consumption model of the screw conveyor [...] Read more.
A new screw conveyor with flexible discrete spiral blades is proposed to solve the problem of particle material gathering between the screw and the tube wall in the traditional screw conveyor. With a theoretical analysis, the power consumption model of the screw conveyor with flexible discrete spiral blades is built. Then, its practicability is verified by simulation and experimental testing. The simulation results show that the increase of the spiral angle will raise the transportation speed of the particles. The diameter of the flexible blades raises with the increase of the power consumption of the screw conveyor. The experimental testing verified the analysis and simulation results. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass
Appl. Sci. 2018, 8(5), 841; https://doi.org/10.3390/app8050841 - 22 May 2018
Cited by 5
Abstract
The prediction of the support pressure (Pi) and the development of the ground reaction curve (GRC) are crucial elements of the convergence–confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are [...] Read more.
The prediction of the support pressure (Pi) and the development of the ground reaction curve (GRC) are crucial elements of the convergence–confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the Pi of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material’s strength in the plastic zone. The analysis also presents separate solutions for the Mohr–Coulomb and Hoek–Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr–Coulomb criteria and 17-5-15-1 for the Hoek–Brown criteria appeared optimum for the prediction of the Pi. On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr–Coulomb criterion and 17-3-12-1 for the Hoek–Brown criterion were found to be the optimum for the prediction of the Pi. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network
Appl. Sci. 2018, 8(3), 405; https://doi.org/10.3390/app8030405 - 09 Mar 2018
Cited by 7
Abstract
An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two [...] Read more.
An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch–catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson’s Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Analysis of Mechanical Properties of Self Compacted Concrete by Partial Replacement of Cement with Industrial Wastes under Elevated Temperature
Appl. Sci. 2018, 8(3), 364; https://doi.org/10.3390/app8030364 - 07 Mar 2018
Cited by 12
Abstract
Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and [...] Read more.
Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and obvious issue for construction industry to develop an alternative remedy as material which can reduce the environmental hazards and enable high-performance strength to the concrete, which would make it durable and efficient for work. A growing trend is being established all over the world to use industrial byproducts and domestic wastes as a useful raw material in construction, as it provides an eco-friendly edge to the construction process and especially for concrete. This study aims to enlighten the use and comparative analysis for the performance of concrete with added industrial byproducts such as Ground Granulated Blast Furnace Slag (GGBFS), Silica fumes (SF) and Marble Powder (MP) in the preparation of SCC. This paper deals with the prediction of mechanical properties (i.e., compressive, tensile and flexural Strength) of self-compacting concrete by considering four major factors such as type of additive, percentage additive replaced, curing days and temperature using Artificial Neural Networks (ANNs). Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Topology Optimisation Using MPBILs and Multi-Grid Ground Element
Appl. Sci. 2018, 8(2), 271; https://doi.org/10.3390/app8020271 - 12 Feb 2018
Cited by 8
Abstract
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the [...] Read more.
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Monitoring Damage Using Acoustic Emission Source Location and Computational Geometry in Reinforced Concrete Beams
Appl. Sci. 2018, 8(2), 189; https://doi.org/10.3390/app8020189 - 26 Jan 2018
Cited by 6
Abstract
Non-destructive testing in reinforced concrete (RC) for damage detection is still limited to date. In monitoring the damage in RC, 18 beam specimens with varying water cement ratios and reinforcements were casted and tested using a four-point bending test. Repeated step loads were [...] Read more.
Non-destructive testing in reinforced concrete (RC) for damage detection is still limited to date. In monitoring the damage in RC, 18 beam specimens with varying water cement ratios and reinforcements were casted and tested using a four-point bending test. Repeated step loads were designed and at each step load acoustic emission (AE) signals were recorded and processed to obtain the acoustic emission source location (AESL). Computational geometry using a convex hull algorithm was used to determine the maximum volume formed by the AESL inside the concrete beam in relation to the load applied. The convex hull volume (CHV) showed good relation to the damage encountered until 60% of the ultimate load at the midspan was reached, where compression in the concrete occurred. The changes in CHV from 20 to 40% and 20 to 60% load were five and 13 times from CHV of 20% load for all beams, respectively. This indicated that the analysis in three dimensions using CHV was sensitive to damage. In addition, a high water-cement ratio exhibited higher CHV formation compared to a lower water-cement ratio due to its ductility where the movement of AESL becomes wider. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems
Appl. Sci. 2017, 7(12), 1318; https://doi.org/10.3390/app7121318 - 18 Dec 2017
Cited by 5
Abstract
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding [...] Read more.
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding a local deviation estimated by the kriging method and a global model approximated by a radial basis function. An expected hypervolume improvement is then computed on the basis of the model uncertainty to determine additional samples that could improve the model accuracy. In the investigation, the proposed approach is applied to two-objective and three-objective optimization test functions. Then, it is applied to aerodynamic airfoil design optimization with two objective functions, namely minimization of aerodynamic drag and maximization of airfoil thickness at the trailing edge. Finally, the proposed method is applied to aerodynamic airfoil design optimization with three objective functions, namely minimization of aerodynamic drag at cruising speed, maximization of airfoil thickness at the trialing edge and maximization of lift at low speed assuming a landing attitude. XFOILis used to investigate the low-fidelity aerodynamic force, and a Reynolds-averaged Navier–Stokes simulation is applied for high-fidelity aerodynamics in conjunction with a high-cost approach. For comparison, multi-objective optimization is carried out using a kriging model only with a high-fidelity solver (single fidelity). The design results indicate that the non-dominated solutions of the proposed method achieve greater data diversity than the optimal solutions of the kriging method. Moreover, the proposed method gives a smaller error than the kriging method. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Elastic Stability of Perforated Plates Strengthened with FRP under Uniaxial Compression
Appl. Sci. 2017, 7(11), 1188; https://doi.org/10.3390/app7111188 - 18 Nov 2017
Cited by 1
Abstract
Openings are frequently introduced in plates for the purpose of inspection, maintenance, service, etc. The presence of openings reduces the buckling and ultimate capacity significantly, and pasting fiber-reinforced polymers (FRP) is an ideal technique for postponing the buckling and increasing the ultimate capacity [...] Read more.
Openings are frequently introduced in plates for the purpose of inspection, maintenance, service, etc. The presence of openings reduces the buckling and ultimate capacity significantly, and pasting fiber-reinforced polymers (FRP) is an ideal technique for postponing the buckling and increasing the ultimate capacity of the plates. In this paper, the finite element (FE) method has been employed to study the buckling stress of the perforated plates strengthened with FRP under uniaxial compression, and several parameters are considered: material’s geometrical and mechanical properties, boundary conditions, plate aspect ratio, hole sizes, and hole position. Then a method of calculating the buckling stress is proposed and modified based on the theory of composite plate and the numerical results. The study shows that, the stiffness modified factor αD, which considers the orthotropic properties of FRP are a function of the reinforcement index ω and hole size d/b for Boundary conditions (BCs) of 4S and 3S1F. And it is recommended to place the big hole close to the middle area of the plate in x-axis. It also shows that for a small hole size, there is little effect of the hole position ey/b on buckling coefficient Ku regardless of the BCs, and that effect becomes more pronounced as d/b increases, so it is recommended to put the holes near the middle of the plate for 4S and the simple support edge for 3S1F in y-axis. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Determination of the Constants of GTN Damage Model Using Experiment, Polynomial Regression and Kriging Methods
Appl. Sci. 2017, 7(11), 1179; https://doi.org/10.3390/app7111179 - 15 Nov 2017
Cited by 5
Abstract
Damage models, particularly the Gurson–Tvergaard–Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time consuming. In the current work, a [...] Read more.
Damage models, particularly the Gurson–Tvergaard–Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time consuming. In the current work, a combination of experimental, numerical simulation and optimization were used to determine the constants. Quasi-static and dynamic tests were carried out on notched specimens. The experimental profiles of the specimens were used to determine the constants. The constants of GTN damage model were identified through the proposed method and using the results of quasi-static tests. Numerical simulation of the dynamic test was performed utilizing the constants obtained from quasi-static experiments. The results showed a high precision in predicting the specimen’s profile in the dynamic testing. The sensitivity analysis was performed on the constants of GTN model to validate the proposed method. Finally, the experiments were simulated using the Johnson–Cook (J–C) damage model and the results were compared to those obtained from GTN damage model. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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Open AccessArticle
Hybrid Prediction Model of the Temperature Field of a Motorized Spindle
Appl. Sci. 2017, 7(10), 1091; https://doi.org/10.3390/app7101091 - 22 Oct 2017
Cited by 4
Abstract
The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve [...] Read more.
The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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