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: 31 May 2019

Special Issue Editor

Guest Editor
Prof. Dr. Panagiotis G. Asteris

Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, Athens, Greece
Website | E-Mail
Interests: applied mathematics and numerical methods; computational mechanics; artificial neural networks

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (21 papers)

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Research

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
Received: 13 April 2019 / Revised: 7 May 2019 / Accepted: 8 May 2019 / Published: 14 May 2019
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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
Received: 5 April 2019 / Revised: 25 April 2019 / Accepted: 25 April 2019 / Published: 5 May 2019
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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
Received: 12 March 2019 / Revised: 15 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
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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
Received: 14 February 2019 / Revised: 7 March 2019 / Accepted: 12 March 2019 / Published: 16 March 2019
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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
Received: 13 February 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 13 March 2019
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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
Received: 27 October 2018 / Revised: 28 December 2018 / Accepted: 30 December 2018 / Published: 10 January 2019
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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
Received: 20 November 2018 / Revised: 26 December 2018 / Accepted: 27 December 2018 / Published: 8 January 2019
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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
Received: 29 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 28 November 2018
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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
Received: 12 September 2018 / Revised: 2 October 2018 / Accepted: 8 October 2018 / Published: 14 October 2018
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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
Received: 10 September 2018 / Revised: 30 September 2018 / Accepted: 1 October 2018 / Published: 9 October 2018
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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
Received: 25 April 2018 / Revised: 6 June 2018 / Accepted: 15 June 2018 / Published: 27 June 2018
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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
Received: 7 April 2018 / Revised: 12 June 2018 / Accepted: 15 June 2018 / Published: 26 June 2018
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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
Received: 13 May 2018 / Revised: 16 May 2018 / Accepted: 17 May 2018 / Published: 22 May 2018
Cited by 2 | PDF Full-text (9979 KB) | HTML Full-text | XML Full-text
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
Received: 1 February 2018 / Revised: 28 February 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
Cited by 4 | PDF Full-text (3792 KB) | HTML Full-text | XML Full-text
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
Received: 29 January 2018 / Revised: 23 February 2018 / Accepted: 24 February 2018 / Published: 7 March 2018
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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
Received: 22 December 2017 / Revised: 6 February 2018 / Accepted: 8 February 2018 / Published: 12 February 2018
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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
Received: 14 November 2017 / Revised: 20 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
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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
Received: 30 October 2017 / Revised: 29 November 2017 / Accepted: 15 December 2017 / Published: 18 December 2017
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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
Received: 18 October 2017 / Revised: 8 November 2017 / Accepted: 13 November 2017 / Published: 18 November 2017
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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
Received: 8 October 2017 / Revised: 9 November 2017 / Accepted: 10 November 2017 / Published: 15 November 2017
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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
Received: 26 September 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 22 October 2017
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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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