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The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.
Iman Faridmehr; Mehdi Nikoo; Mohammad Baghban; Raffaele Pucinotti. Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections. Buildings 2021, 11, 229 .
AMA StyleIman Faridmehr, Mehdi Nikoo, Mohammad Baghban, Raffaele Pucinotti. Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections. Buildings. 2021; 11 (6):229.
Chicago/Turabian StyleIman Faridmehr; Mehdi Nikoo; Mohammad Baghban; Raffaele Pucinotti. 2021. "Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections." Buildings 11, no. 6: 229.
The number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete.
Nasrin Aalimahmoody; Chiara Bedon; Nasim Hasanzadeh-Inanlou; Amir Hasanzade-Inallu; Mehdi Nikoo. BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study. Infrastructures 2021, 6, 80 .
AMA StyleNasrin Aalimahmoody, Chiara Bedon, Nasim Hasanzadeh-Inanlou, Amir Hasanzade-Inallu, Mehdi Nikoo. BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study. Infrastructures. 2021; 6 (6):80.
Chicago/Turabian StyleNasrin Aalimahmoody; Chiara Bedon; Nasim Hasanzadeh-Inanlou; Amir Hasanzade-Inallu; Mehdi Nikoo. 2021. "BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study." Infrastructures 6, no. 6: 80.
Eco-friendly and sustainable materials that are cost-effective, while having a reduced carbon footprint and energy consumption, are in great demand by the construction industry worldwide. Accordingly, alkali-activated materials (AAM) composed primarily of industrial byproducts have emerged as more desirable alternatives to ordinary Portland cement (OPC)-based concrete. Hence, this study investigates the cradle-to-gate life-cycle assessment (LCA) of ternary blended alkali-activated mortars made with industrial byproducts. Moreover, the embodied energy (EE), which represents an important parameter in cradle-to-gate life-cycle analysis, was investigated for 42 AAM mixtures. The boundary of the cradle-to-gate system was extended to include the mechanical and durability properties of AAMs on the basis of performance criteria. Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs. Considering the lack of systematic research on the cradle-to-gate LCA of AAMs in the literature, the results of this research provide new insights into the assessment of the environmental impact of AAM made with industrial byproducts. The final weight and bias values of the AAN model can be used to design AAM mixtures with targeted mechanical properties and CO2 emission considering desired amounts of industrial byproduct utilization in the mixture.
Iman Faridmehr; Moncef Nehdi; Mehdi Nikoo; Ghasan Huseien; Togay Ozbakkaloglu. Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts. Materials 2021, 14, 2401 .
AMA StyleIman Faridmehr, Moncef Nehdi, Mehdi Nikoo, Ghasan Huseien, Togay Ozbakkaloglu. Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts. Materials. 2021; 14 (9):2401.
Chicago/Turabian StyleIman Faridmehr; Moncef Nehdi; Mehdi Nikoo; Ghasan Huseien; Togay Ozbakkaloglu. 2021. "Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts." Materials 14, no. 9: 2401.
Top and seat beam-to-column connections are commonly designed to transfer gravitational loads of simply supported steel beams. Nevertheless, the flexural resistance characteristics of these type of connections should be properly taken into account for design, when a reliable analysis of semi-rigid steel structures is desired. In this research paper, different component-based mechanical models from Eurocode 3 (EC3) and a literature proposal (by Kong and Kim, 2017) are considered to evaluate the initial stiffness (Sj,ini ) and ultimate moment capacity (Mn ) of top-seat angle connections with double web angles (TSACWs). An optimized artificial neural network (ANN) model based on the artificial bee colony (ABC) algorithm is proposed in this paper to acquire an informational model from the available literature database of experimental test measurements on TSACWs. In order to evaluate the expected effect of each input parameter (such as the thickness of top flange cleat, the bolt size, etc.) on the mechanical performance and overall moment–rotation (M–θ) response of the selected connections, a sensitivity analysis is presented. The collected comparative results prove the potential of the optimized ANN approach for TSACWs, as well as its accuracy and reliability for the prediction of the characteristic (M–θ) features of similar joints. For most of the examined configurations, higher accuracy is found from the ANN estimates, compared to Eurocode 3- or Kong et al.-based formulations.
Iman Faridmehr; Mehdi Nikoo; Raffaele Pucinotti; Chiara Bedon. Application of Component-Based Mechanical Models and Artificial Intelligence to Bolted Beam-to-Column Connections. Applied Sciences 2021, 11, 2297 .
AMA StyleIman Faridmehr, Mehdi Nikoo, Raffaele Pucinotti, Chiara Bedon. Application of Component-Based Mechanical Models and Artificial Intelligence to Bolted Beam-to-Column Connections. Applied Sciences. 2021; 11 (5):2297.
Chicago/Turabian StyleIman Faridmehr; Mehdi Nikoo; Raffaele Pucinotti; Chiara Bedon. 2021. "Application of Component-Based Mechanical Models and Artificial Intelligence to Bolted Beam-to-Column Connections." Applied Sciences 11, no. 5: 2297.
Alkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al2O3, SiO2, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption.
Iman Faridmehr; Chiara Bedon; Ghasan Huseien; Mehdi Nikoo; Mohammad Baghban. Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials. Sustainability 2021, 13, 2062 .
AMA StyleIman Faridmehr, Chiara Bedon, Ghasan Huseien, Mehdi Nikoo, Mohammad Baghban. Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials. Sustainability. 2021; 13 (4):2062.
Chicago/Turabian StyleIman Faridmehr; Chiara Bedon; Ghasan Huseien; Mehdi Nikoo; Mohammad Baghban. 2021. "Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials." Sustainability 13, no. 4: 2062.
In this study, the compressive strength of cementitious composite containing ground granulated blast furnace slag (GGBFS) has been predicted. For this purpose, the intelligent models: the self-organizing feature map (SOFM) and the artificial neural network (ANN) were used and compared. A database containing 84 sets of data was created based on the time and mixture proportions of concrete, as well as on nondestructive ultrasonic pulse velocity measurements. It was proved that the developed model of predicting the compressive strength of the green cementitious composites containing GGBFS was accurate. It was also simple as it contained only three parameters that were used as input variables. The novelty of this research is the fact that they can be performed on existing structures, not only after 28 days, but also at early ages (3 and 7 days) and much longer periods (after 150 and 180 days). This makes this method more universal and increases the possibility of it being useful for construction practice.
Sławomir Czarnecki; Mohd Shariq; Mehdi Nikoo; Łukasz Sadowski. An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements. Measurement 2021, 172, 108951 .
AMA StyleSławomir Czarnecki, Mohd Shariq, Mehdi Nikoo, Łukasz Sadowski. An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements. Measurement. 2021; 172 ():108951.
Chicago/Turabian StyleSławomir Czarnecki; Mohd Shariq; Mehdi Nikoo; Łukasz Sadowski. 2021. "An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements." Measurement 172, no. : 108951.
Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest; however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.
Amir Hasanzade-Inallu; Panam Zarfam; Mehdi Nikoo. Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP. Journal of Central South University 2019, 26, 3156 -3174.
AMA StyleAmir Hasanzade-Inallu, Panam Zarfam, Mehdi Nikoo. Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP. Journal of Central South University. 2019; 26 (11):3156-3174.
Chicago/Turabian StyleAmir Hasanzade-Inallu; Panam Zarfam; Mehdi Nikoo. 2019. "Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP." Journal of Central South University 26, no. 11: 3156-3174.
The adaptive neuro-fuzzy inference systems (ANFIS) are widely used in the concrete technology. In this research, the compressive strength of light weight concrete was determined. To this end, the scoria percentage and curing day variables were used as the input parameters, and compressive strength and tensile strength were used as the output parameters. In addition, 100 patterns were used, 70% of which were used for training and 30% were used for testing. To assess the precision of the neuro-fuzzy system, it was compared using two linear regression models. The comparisons were carried out in the training and testing phases. Research results revealed that the neuro-fuzzy systems model offers more potential, flexibility, and precision than the statistical models. 目前,自适应神经模糊推理系统(ANFIS)在混凝土技术中得到了广泛的应用。本研究利用神经 模糊系统确定了轻量化混凝土的抗压强度。以废渣百分率和固化天数作为网络的输入参数,以抗压强 度和抗拉强度作为输出参数。实验选用了100 个模式,其中70%用于训练,30%用于测试。为了评估 神经模糊系统的精度,比较了神经模糊系统和统计模型(LR)两种线性回归模型的训练和测试阶段。结 果表明,神经模糊系统模型比统计模型具有更大的潜力、适应性性和精度。
Seyed Vahid Razavi Tosee; Mehdi Nikoo. Neuro-fuzzy systems in determining light weight concrete strength. Journal of Central South University 2019, 26, 2906 -2914.
AMA StyleSeyed Vahid Razavi Tosee, Mehdi Nikoo. Neuro-fuzzy systems in determining light weight concrete strength. Journal of Central South University. 2019; 26 (10):2906-2914.
Chicago/Turabian StyleSeyed Vahid Razavi Tosee; Mehdi Nikoo. 2019. "Neuro-fuzzy systems in determining light weight concrete strength." Journal of Central South University 26, no. 10: 2906-2914.
The aim of this study was to develop a nature-inspired metaheuristic method to predict the creep strain of green concrete containing ground granulated blast furnace slag (GGBFS) using an artificial neural network (ANN)model. The firefly algorithm (FA) was used to optimize the weights in the ANN. For this purpose, the cement content, GGBFS content, water-to-binder ratio, fine aggregate content, coarse aggregate content, slump, the compaction factor of concrete and the age after loading were used as the input parameters, and in turn, the creep strain (εcr) of the GGBFS concrete was considered as the output parameters. To evaluate the accuracy of the FA-ANN model, it was compared with the well-known genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). Results indicated that the ANNs model, in which the weights were optimized by the FA, were more capable, flexible and precise than other optimization algorithms in predicting the εcr of GGBFS concrete.
Łukasz Sadowski; Mehdi Nikoo; Mohd Shariq; Ebrahim Joker; Sławomir Czarnecki; Mohd Nikoo. The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag. Materials 2019, 12, 293 .
AMA StyleŁukasz Sadowski, Mehdi Nikoo, Mohd Shariq, Ebrahim Joker, Sławomir Czarnecki, Mohd Nikoo. The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag. Materials. 2019; 12 (2):293.
Chicago/Turabian StyleŁukasz Sadowski; Mehdi Nikoo; Mohd Shariq; Ebrahim Joker; Sławomir Czarnecki; Mohd Nikoo. 2019. "The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag." Materials 12, no. 2: 293.
The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence algorithm for optimization, which has already been successfully applied for the training of artificial neural network (ANN) models. This paper thoroughly explores the performance of the ABC algorithm for optimizing the connection weights of feed-forward (FF) neural network models, aiming to accurately determine one of the most critical parameters in reinforced concrete structures, namely the fundamental period of vibration. Specifically, this study focuses on the determination of the vibration period of reinforced concrete infilled framed structures, which is essential to earthquake design, using feed-forward ANNs. To this end, the number of storeys, the number of spans, the span length, the infill wall panel stiffness, and the percentage of openings within the infill panel are selected as input parameters, while the value of vibration period is the output parameter. The accuracy of the FF–ABC model is verified through comparison with available formulas in the literature. The results indicate that the artificial neural network, the weights of which had been optimized via the ABC algorithm, exhibits greater ability, flexibility and accuracy in comparison with statistical models.
Panagiotis G. Asteris; Mehdi Nikoo. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Computing and Applications 2019, 31, 4837 -4847.
AMA StylePanagiotis G. Asteris, Mehdi Nikoo. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Computing and Applications. 2019; 31 (9):4837-4847.
Chicago/Turabian StylePanagiotis G. Asteris; Mehdi Nikoo. 2019. "Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures." Neural Computing and Applications 31, no. 9: 4837-4847.
This paper examines the robustness of a feed forward artificial neural network combined with an artificial bee colony algorithm (FF-ABC) in the prediction of chloride penetration of self-consolidating concretes. To this end, several self-consolidating concrete mixes were made using various mix proportions, and their rapid chloride penetrations (RCPT) were measured. The mix proportions and RCPT results were used as input and output variables, respectively, to train and test the proposed method. To verify accuracy of the FF-ABC model, its performance was compared to linear regression, genetic algorithm (GA), and particle swarm optimization (PSO) models. This comparison was conducted in three stages of training, validation, and testing. Results of this study indicate higher reliability of the FF-ABC model in comparison with the statistical, GA, and PSO models.
Meysam Najimi; Nader Ghafoori; Mehdi Nikoo. Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm. Journal of Building Engineering 2018, 22, 216 -226.
AMA StyleMeysam Najimi, Nader Ghafoori, Mehdi Nikoo. Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm. Journal of Building Engineering. 2018; 22 ():216-226.
Chicago/Turabian StyleMeysam Najimi; Nader Ghafoori; Mehdi Nikoo. 2018. "Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm." Journal of Building Engineering 22, no. : 216-226.
Maintenance is essential to ensure safe operation of equipment in normal conditions. Therefore, managers must identify the relative priorities and equipment maintenance requirements. Moreover, based on the results of equipment vulnerability assessments, maintenance programs can be developed and managed properly. There are different methods and techniques in the process of risk assessment and management and vulnerability of equipment. Seventy-six samples with different properties have been used in this study. Networks used in this study are self-organizing networks with constant weight, which include Kohonen networks. For this purpose, operation impact, operation flexibility, maintenance cost, impact of safety and environment and frequency parameters had been considered as input; and using this model, the risk level is calculated. Utilizing genetic algorithms, the structures of all self-organizing systems are optimized. In order to evaluate the accuracy of the model, we compare it with the fuzzy model, and the results indicate that self-organizing systems optimized with the genetic algorithm have higher ability, flexibility and accuracy than the fuzzy model in predicting risk.
F. Jaderi; Zelina Z. Ibrahim; Mehdi Nikoo; Mohammad Nikoo. Utilizing self-organization systems for modeling and managing risk based on maintenance and repair in petrochemical industries. Soft Computing 2018, 23, 6379 -6390.
AMA StyleF. Jaderi, Zelina Z. Ibrahim, Mehdi Nikoo, Mohammad Nikoo. Utilizing self-organization systems for modeling and managing risk based on maintenance and repair in petrochemical industries. Soft Computing. 2018; 23 (15):6379-6390.
Chicago/Turabian StyleF. Jaderi; Zelina Z. Ibrahim; Mehdi Nikoo; Mohammad Nikoo. 2018. "Utilizing self-organization systems for modeling and managing risk based on maintenance and repair in petrochemical industries." Soft Computing 23, no. 15: 6379-6390.
An artificial neural network (ANN) is used to model the frequency of the first mode, using the beam length, the moment of inertia, and the load applied on the beam as input parameters on a database of 100 samples. Three different heuristic optimization methods are used to train the ANN: genetic algorithm (GA), particle swarm optimization algorithm and imperialist competitive algorithm. The suitability of these algorithms in training ANN is determined based on accuracy and runtime performance. Results show that, in determining the natural frequency of cantilever beams, the ANN model trained using GA outperforms the other models in terms of accuracy.
Mehdi Nikoo; Marijana Hadzima-Nyarko; Emmanuel Karlo Nyarko; Mohammad Nikoo. Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search. Applied Artificial Intelligence 2018, 32, 309 -334.
AMA StyleMehdi Nikoo, Marijana Hadzima-Nyarko, Emmanuel Karlo Nyarko, Mohammad Nikoo. Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search. Applied Artificial Intelligence. 2018; 32 (3):309-334.
Chicago/Turabian StyleMehdi Nikoo; Marijana Hadzima-Nyarko; Emmanuel Karlo Nyarko; Mohammad Nikoo. 2018. "Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search." Applied Artificial Intelligence 32, no. 3: 309-334.
In this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision.
Panagiotis G. Asteris; Saeed Nozhati; Mehdi Nikoo; Liborio Cavaleri; Mohammad Nikoo. Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mechanics of Advanced Materials and Structures 2018, 26, 1146 -1153.
AMA StylePanagiotis G. Asteris, Saeed Nozhati, Mehdi Nikoo, Liborio Cavaleri, Mohammad Nikoo. Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mechanics of Advanced Materials and Structures. 2018; 26 (13):1146-1153.
Chicago/Turabian StylePanagiotis G. Asteris; Saeed Nozhati; Mehdi Nikoo; Liborio Cavaleri; Mohammad Nikoo. 2018. "Krill herd algorithm-based neural network in structural seismic reliability evaluation." Mechanics of Advanced Materials and Structures 26, no. 13: 1146-1153.
Soils treatment is affected by various factors such as density, moisture content and mineral composition of soil and different percentages of materials in soil. Lime soil as a suitable and inexpensive material has been used for decades to stabilize in civil engineering projects; however, the effect of adding fume and curing temperature on strength and stability parameters of the mixture seldom been studied. In this study, soil and water has been studied from Dokhtar Borji in Hosseinieh city in Iran. Based on a laboratory study, we dealt with evaluating the physical and mechanical properties of soils and chemical properties of soil and water. The cylindrical samples of different mixtures of soil- lime- fume were modified using the AASHTO method and compressive strength testing of 7-, 14- and 28-day samples were conducted according to ASTM standards at 27 °C. Analysis was conducted in SAS (Statistical Analysis System) software. Results indicated that the increase in average compressive strength from 7 to 14 and from 14 to 28 days were 62 and 53.57%, respectively. Therefore, by increasing the number of curing days from 7 to 14, the percentage of the compressive strength is at its highest. The study also provided a linear regression equation that determines compressive strength with an accuracy of 95.1%
Mahmoud Nematzadeh; Panam Zarfam; Mehdi Nikoo. Investigating laboratory parameters of the resistance of different mixtures of soil – lime – fume using the curing and administrative method. Case Studies in Construction Materials 2017, 7, 263 -279.
AMA StyleMahmoud Nematzadeh, Panam Zarfam, Mehdi Nikoo. Investigating laboratory parameters of the resistance of different mixtures of soil – lime – fume using the curing and administrative method. Case Studies in Construction Materials. 2017; 7 ():263-279.
Chicago/Turabian StyleMahmoud Nematzadeh; Panam Zarfam; Mehdi Nikoo. 2017. "Investigating laboratory parameters of the resistance of different mixtures of soil – lime – fume using the curing and administrative method." Case Studies in Construction Materials 7, no. : 263-279.
Faezehossadat Khademi; Mahmoud Akbari; Mehdi Nikoo. Displacement determination of concrete reinforcement building using data-driven models. International Journal of Sustainable Built Environment 2017, 6, 400 -411.
AMA StyleFaezehossadat Khademi, Mahmoud Akbari, Mehdi Nikoo. Displacement determination of concrete reinforcement building using data-driven models. International Journal of Sustainable Built Environment. 2017; 6 (2):400-411.
Chicago/Turabian StyleFaezehossadat Khademi; Mahmoud Akbari; Mehdi Nikoo. 2017. "Displacement determination of concrete reinforcement building using data-driven models." International Journal of Sustainable Built Environment 6, no. 2: 400-411.
A disadvantage of using linear polarization resistance (LPR) in the measurement of corrosion current density is the need to partially destroy a concrete cover. In this article, a new technique of predicting the corrosion current density in reinforced concrete using a self-organizing feature map (SOFM) is presented. For this purpose, air temperature, and also the parameters determined by the resistivity four-probe method and galvanostatic resistivity measurements, were employed as input variables. The corrosion current density, predicted by the destructive LPR method, was employed as the output variable. The weights of the SOFM were optimized using the genetic algorithm (GA). To evaluate the accuracy of the SOFM, a comparison with the radial basis function (RBF) and linear regression (LR) was performed. The results indicate that the SOFM–GA model has a higher ability, flexibility, and accuracy than the RBF and LR.
Mehdi Nikoo; Łukasz Sadowski; Mohammad Nikoo. Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map. Coatings 2017, 7, 160 .
AMA StyleMehdi Nikoo, Łukasz Sadowski, Mohammad Nikoo. Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map. Coatings. 2017; 7 (10):160.
Chicago/Turabian StyleMehdi Nikoo; Łukasz Sadowski; Mohammad Nikoo. 2017. "Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map." Coatings 7, no. 10: 160.
The paper presents the use of a self-organizing feature map (SOFM) for determining damage in reinforced concrete frames with shear walls. For this purpose, a concrete frame with a shear wall was subjected to nonlinear dynamic analysis. The SOFM was optimized using the genetic algorithm (GA) in order to determine the number of layers, number of nodes in the hidden layer, transfer function type, and learning algorithm. The obtained model was compared with linear regression (LR) and nonlinear regression (NonLR) models and also the radial basis function (RBF) of a neural network. It was concluded that the SOFM, when optimized with the GA, has more strength, flexibility, and accuracy.1. IntroductionDamage to concrete structures mainly occurs because of inadequate management, incorrect maintenance, overloading, exposure to chemical components, climatic factors, and also extra loads such as earthquakes [1]. As mentioned by Nikoo et al. [2], earthquakes are the most devastating of these factors. Their destruction mechanism can cause extraordinary damage to a structure. In the last few years, much attention has been given to the use of artificial neural networks (ANNs) for solving various civil engineering problems [3–8].In the self-organizing feature map (SOFM), cells are organized in various sensual areas with regular and significant computational maps [6, 9]. As described by Kohonen [9], processor units are placed within the nodes of a one-dimensional (or more) network and are regulated in a competitive learning process [9, 10]. Therefore, the SOFM can be seen as a topographic map for input models, in which the units’ locations correspond to the inherent features of the input models. Competitive learning is applied in such networks, and in each step the units compete in order to be activated. At the end of the initial step of this competition only one unit wins and its weights are changed differently when compared to the weights of other units. This kind of learning is called unsupervised learning [6]. In previous papers, the SOFM was described in more detail [6, 10].In the genetic algorithm (GA), chromosomes with high competence have a higher chance of repeating in the selected population of the replication process. The basic operators of the GA are reproduction, crossover, and mutation [11]. The GA ends when certain criteria, such as a certain number of generations or the average standard deviation performance of individuals, are fulfilled [12].The main objective of this study was to evaluate the abilities of the SOFM in determining damage in reinforced concrete frames with shear walls. The SOFM was optimized using the GA in order to determine the number of layers, number of nodes in the hidden layer, transfer function type, and learning algorithm. The obtained model was compared with linear regression (LR), nonlinear regression (NonLR), and the radial basis function (RBF) of a neural network.2. A Short Description of the Self-Organizing Feature Map (SOFM)In the SOFM, the competitive learning method is used for training and is based on specific characteristics of a developed human brain. The cells in the human brain are organized in various sensual areas with regular and significant computational maps [6, 9].In the SOFM, processor units are placed within the nodes of a one-dimensional or two-dimensional network (Figure 1). These units are regulated in a competitive learning process and compared to the input models [9, 10]. Therefore, the SOFM can be seen as a topographic map for the input models, in which the units’ locations correspond to the inherent features of the input models. Competitive learning is applied in such networks, and in each step the units compete in order to be activated. At the end of the initial step of this competition, only one unit wins and its weights are changed differently when compared to the weights of other units. This kind of learning is called unsupervised learning [6].Figure 1: Structural model of (a) a one-dimensional network [9] and (b) a two-dimensional network [14].3. The Park and Ang Damage IndexOne of the most useful methods proposed for quantifying the calculation of damage in concrete structures is the Park and Ang model. As mentioned by Valles et al. [13], it is defined as follows [15]: where is the maximum response of deformation under seismic load. is the calculated yield strength. is the ultimate deformation under uniform loading. is the hysteric absorbed energy. is the resistance reduction parameter according to hysteric energy.The Park and Ang index value is between 0 and 1. The damage range is shown in Table 1.Table 1: Typical damage range in concrete reinforcement frames [13].4. Experimental SetupTo determine the distribution function for the Park and Ang damage index, a concrete frame with a shear wall was selected. Lateral loading of the mentioned structure was then applied. In the next step, the structure was designed. The data associated with reinforced concrete frames with shear walls is listed in Table 2.Table 2: Data associated with reinforced concrete frames with shear walls [2, 16].One of the main parameters influencing the input energy of structures is the earthquake accelerogram applied in seismic analysis. The extent of input energy applied to the structure is more dependent on input mapping than its structural characteristics [2]. In this research, thirty earthquakes were used for nonlinear dynamic analysis, as listed in Table 3. After the analysis, the overall Park and Ang damage index was extracted using version 4.0 of IDARC 2D software.Table 3: Seismic characteristics used in the study [2, 16].The input parameters in this research include the following: peak ground acceleration (PGA); input time of the earthquake to a structure; time; frequency; input acceleration to the building (Acc); and also displacement. The output parameter is the Park and Ang damage index. Table 4 represents the statistical characteristics of the parameters.Table 4: Selected statistical characteristics of the parameters.5. Experimental Results5.1. Performance Evaluation of the SOFMThree Kohonen ANNs (Square, Line, and Diamond) were employed in this research for the SOFM. From 412 sets of data, 70% (288 sets) were used for training, 15% (62 sets) were used for validation, and 15% (62 sets) were used for testing of the ANN. Different stimulation functions, including LinearTanhAxon, LinearAxon, and TanhAxon, were used. Table 5 shows the characteristics of the selected SOFM models.Table 5: The characteristics of the selected SOFM models.Table 6 presents the optimized structure of the SOFM models for training, validation and testing. Table 7 presents the statistical results of different models in the SOFM. As can be seen from these tables, the SOFM1 model enjoys the highest values of the correlation coefficient for prediction of the Park and Ang damage index for training, validation, and testing.Table 6: The optimized structure of SOFM models in training, validation, and testing.Table 7: Statistical results of different SOFM models.Comparison of the Park and Ang damage index and calculated data for training, validation, and testing for each of the laboratory samples is presented in Figure 2.Figure 2: Comparison of the Park and Ang damage index and calculated data for (a) training, (b) validation, and (c) testing.The obtained values of correlation coefficient for the Park and Ang damage index for the SOFM1 model were 0.9330, 0.9216, and 0.9221 for training, validation, and testing, respectively. Additionally, the values of mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are less than those for the two other models. Considering the above, MSE versus epoch for the SOFM1 model is presented in Figure 3.Figure 3: MSE versus epoch in training and validation of the SOFM1 model.Considering the above, the best ANN for adaptation of input data is the SOFM with a 5 × 5 structure (Figure 4).Figure 4: Structure for the adaptation of input data in training and validation of the SOFM1 model.In addition, the impact of distances and weights of the neighborhood in a 5 × 5 structure in the SOFM1 model is presented in Figure 5.Figure 5: The impact of distances and weights of the neighborhood in a 5 × 5 structure in the SOFM1 model.5.2. Comparison of Selected SOFM Models5.2.1. Linear Regression (LR)First, linear regression (LR) was used [15]. LR models are based on a data oriented technique, where the collected data is directly associated with each other. The process behind this data is not considered. In a specific form of LR, data is modeled using linear predictor functions. Unknown model parameters are then estimated from the data [16]. In LR, two or more independent variables have a major effect on the dependent variable shown in equation where is the dependent variable; , , etc. are the independent variables; and , , , etc. are the equation regression coefficients. In this research, various models of LR are investigated using MINITAB software. The best LR model, which was more coordinated with damage data, was obtained using In the above equation, is the damage to the entire frame, is the PGA variable, is the input time variable, is the time variable, is the frequency variable, is the acceleration variable, and is the displacement variable. The analysis results of the three LR models are presented in Table 8 and the results obtained from the statistical indices are presented in Table 9. Figure 6 shows the results arising from different LR models.Table 8: The structure of LR models in training, validation, and testing.Table 9: Statistical results of different LR models.Figure 6: Comparison of the Park and Ang damage index obtained using LR and calculated data for (a) training, (b) validation, and (c) testing.In the LR1 model, the values of are equal to 0.8925, 0.9098, and 0.8924 for training, validation, and testing, respectively. The values of MAE, MSE, and RM
Mehdi Nikoo; Łukasz Sadowski; Faezehossadat Khademi; Mohammad Nikoo. Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map. Applied Computational Intelligence and Soft Computing 2017, 2017, 1 -10.
AMA StyleMehdi Nikoo, Łukasz Sadowski, Faezehossadat Khademi, Mohammad Nikoo. Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map. Applied Computational Intelligence and Soft Computing. 2017; 2017 ():1-10.
Chicago/Turabian StyleMehdi Nikoo; Łukasz Sadowski; Faezehossadat Khademi; Mohammad Nikoo. 2017. "Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map." Applied Computational Intelligence and Soft Computing 2017, no. : 1-10.
The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs) and the imperialist competitive algorithm (ICA). The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT) methods: the impact-echo method (I-E) and the impulse response method (IR). In this research, the ICA has been used to optimize the weights of the ANN. The combined ICA-ANN model has been compared to the genetic algorithm (GA) and particle swarm optimization (PSO) to evaluate its accuracy. The results showed that the ICA-ANN model outperforms other techniques when testing datasets in terms of both effectiveness and efficiency. As presented in the validation stage, it is possible to reliably map the adhesion level on a tested surface without local damage to the latter.
Łukasz Sadowski; Mehdi Nikoo; Mohammad Nikoo. Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites. Coatings 2017, 7, 49 .
AMA StyleŁukasz Sadowski, Mehdi Nikoo, Mohammad Nikoo. Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites. Coatings. 2017; 7 (4):49.
Chicago/Turabian StyleŁukasz Sadowski; Mehdi Nikoo; Mohammad Nikoo. 2017. "Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites." Coatings 7, no. 4: 49.
Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.
Faezehossadat Khademi; Mahmoud Akbari; Sayed Mohammadmehdi Jamal; Mehdi Nikoo. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering 2016, 11, 90 -99.
AMA StyleFaezehossadat Khademi, Mahmoud Akbari, Sayed Mohammadmehdi Jamal, Mehdi Nikoo. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering. 2016; 11 (1):90-99.
Chicago/Turabian StyleFaezehossadat Khademi; Mahmoud Akbari; Sayed Mohammadmehdi Jamal; Mehdi Nikoo. 2016. "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete." Frontiers of Structural and Civil Engineering 11, no. 1: 90-99.