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Prof. Panagiotis G. Asteris
School of Pedagogical & Technological Education, Athens, Greece

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Research Keywords & Expertise

0 Computational Mechanics
0 Earthquake Engineering
0 Structural Engineering
0 Soft Computing Techniques
0 Masonry Structures

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Masonry Structures
Machine Learning & Artificial Intelligence
Soft Computing Techniques
Structural Engineering

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Journal article
Published: 25 July 2021 in Sustainability
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In this research, a new machine-learning approach was proposed to evaluate the effects of eight input parameters (surface area, relative compactness, wall area, overall height, roof area, orientation, glazing area distribution, and glazing area) on two output parameters, namely, heating load (HL) and cooling load (CL), of the residential buildings. The association strength of each input parameter with each output was systematically investigated using a variety of basic statistical analysis tools to identify the most effective and important input variables. Then, different combinations of data were designed using the intelligent systems, and the best combination was selected, which included the most optimal input data for the development of stacking models. After that, various machine learning models, i.e., XGBoost, random forest, classification and regression tree, and M5 tree model, were applied and developed to predict HL and CL values of the energy performance of buildings. The mentioned techniques were also used as base techniques in the forms of stacking models. As a result, the XGboost-based model achieved a higher accuracy level (HL: coefficient of determination, R2 = 0.998; CL: R2 = 0.971) with a lower system error (HL: root mean square error, RMSE = 0.461; CL: RMSE = 1.607) than the other developed models in predicting both HL and CL values. Using new stacking-based techniques, this research was able to provide alternative solutions for predicting HL and CL parameters with appropriate accuracy and runtime.

ACS Style

Ahmed Mohammed; Panagiotis Asteris; Mohammadreza Koopialipoor; Dimitrios Alexakis; Minas Lemonis; Danial Armaghani. Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings. Sustainability 2021, 13, 8298 .

AMA Style

Ahmed Mohammed, Panagiotis Asteris, Mohammadreza Koopialipoor, Dimitrios Alexakis, Minas Lemonis, Danial Armaghani. Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings. Sustainability. 2021; 13 (15):8298.

Chicago/Turabian Style

Ahmed Mohammed; Panagiotis Asteris; Mohammadreza Koopialipoor; Dimitrios Alexakis; Minas Lemonis; Danial Armaghani. 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings." Sustainability 13, no. 15: 8298.

Application of soft computing
Published: 15 July 2021 in Soft Computing
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The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM’s performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were, respectively, defined as decision variables and objective function for the WOA technique to find the optimum values of PR. As a consequence of optimizing the GEP equation, the maximum value of PR rose from 3.75 m/h to 4.022 m/h, equivalent to an increase of 7.25% in PR value. The findings of this study verified the applicability of the proposed hybrid GEP and WOA approach in the site investigation phase of tunneling projects constructed by TBMs.

ACS Style

Zimu Li; Behnam Yazdani Bejarbaneh; Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial Jahed Armaghani; M. M. Tahir. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Computing 2021, 25, 11877 -11895.

AMA Style

Zimu Li, Behnam Yazdani Bejarbaneh, Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, M. M. Tahir. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Computing. 2021; 25 (17):11877-11895.

Chicago/Turabian Style

Zimu Li; Behnam Yazdani Bejarbaneh; Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial Jahed Armaghani; M. M. Tahir. 2021. "A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass." Soft Computing 25, no. 17: 11877-11895.

Journal article
Published: 08 July 2021 in Alexandria Engineering Journal
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Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research shortcomings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful features of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self- adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are studied using partial modal data and noisy conditions. The proposed technique demonstrates outstanding performance and can be recommended to solve continuous optimization problems.

ACS Style

Nizar Faisal Alkayem; Lei Shen; Panagiotis G. Asteris; Milan Sokol; Zhiqiang Xin; Maosen Cao. A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment. Alexandria Engineering Journal 2021, 1 .

AMA Style

Nizar Faisal Alkayem, Lei Shen, Panagiotis G. Asteris, Milan Sokol, Zhiqiang Xin, Maosen Cao. A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment. Alexandria Engineering Journal. 2021; ():1.

Chicago/Turabian Style

Nizar Faisal Alkayem; Lei Shen; Panagiotis G. Asteris; Milan Sokol; Zhiqiang Xin; Maosen Cao. 2021. "A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment." Alexandria Engineering Journal , no. : 1.

Journal article
Published: 04 July 2021 in Engineering with Computers
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ACS Style

Tien-Thinh Le; Panagiotis G. Asteris; Minas E. Lemonis. Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques. Engineering with Computers 2021, 1 .

AMA Style

Tien-Thinh Le, Panagiotis G. Asteris, Minas E. Lemonis. Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques. Engineering with Computers. 2021; ():1.

Chicago/Turabian Style

Tien-Thinh Le; Panagiotis G. Asteris; Minas E. Lemonis. 2021. "Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques." Engineering with Computers , no. : 1.

Original article
Published: 27 June 2021 in Neural Computing and Applications
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A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop a novel hybrid intelligent system, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the imperialism competitive algorithm (ICA), ANFIS-PNN-ICA for prediction of TBM performance. In fact, the role of ICA in this hybrid system is to optimize the membership functions obtained by ANFIS-PNN model for receiving a higher level of performance prediction. Based on previously published works, seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength, rock mass weathering, the uniaxial compressive strength, revolution per minute and thrust force were set as inputs to predict TBM performance. Together with the ANFIS-PNN-ICA model, two single model of PNN and ANFIS were also constructed for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of several performance indices, e.g., correlation coefficient (R). R values of (0.9642, 0.9654 and 1), (0.9482, 0.9671 and 0.9778) and (0.9652, 0.9642, 0.9898) were obtained for training, testing and all datasets of PNN, ANFIS and ANFIS-PNN-ICA models, respectively. These results revealed that the greater prediction capacity can be provided by the ANFIS-PNN-ICA predictive model compared to ANFIS and PNN models and this hybrid intelligent model can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.

ACS Style

Hooman Harandizadeh; Danial Jahed Armaghani; Panagiotis G. Asteris; Amir H. Gandomi. TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm. Neural Computing and Applications 2021, 1 -31.

AMA Style

Hooman Harandizadeh, Danial Jahed Armaghani, Panagiotis G. Asteris, Amir H. Gandomi. TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm. Neural Computing and Applications. 2021; ():1-31.

Chicago/Turabian Style

Hooman Harandizadeh; Danial Jahed Armaghani; Panagiotis G. Asteris; Amir H. Gandomi. 2021. "TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm." Neural Computing and Applications , no. : 1-31.

Journal article
Published: 14 June 2021 in IEEE Access
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Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.

ACS Style

Bo Ke; Manoj Khandelwal; Panagiotis G. Asteris; Athanasia D. Skentou; Anna Mamou; Danial Jahed Armaghani. Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models. IEEE Access 2021, 9, 91347 -91360.

AMA Style

Bo Ke, Manoj Khandelwal, Panagiotis G. Asteris, Athanasia D. Skentou, Anna Mamou, Danial Jahed Armaghani. Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models. IEEE Access. 2021; 9 ():91347-91360.

Chicago/Turabian Style

Bo Ke; Manoj Khandelwal; Panagiotis G. Asteris; Athanasia D. Skentou; Anna Mamou; Danial Jahed Armaghani. 2021. "Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models." IEEE Access 9, no. : 91347-91360.

Journal article
Published: 25 May 2021 in Transportation Geotechnics
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This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 97.27% of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40–75.97 and which is made available as supplementary material.

ACS Style

Panagiotis G. Asteris; Anna Mamou; Mohsen Hajihassani; Mahdi Hasanipanah; Mohammadreza Koopialipoor; Tien-Thinh Le; Navid Kardani; Danial J. Armaghani. Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transportation Geotechnics 2021, 29, 100588 .

AMA Style

Panagiotis G. Asteris, Anna Mamou, Mohsen Hajihassani, Mahdi Hasanipanah, Mohammadreza Koopialipoor, Tien-Thinh Le, Navid Kardani, Danial J. Armaghani. Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transportation Geotechnics. 2021; 29 ():100588.

Chicago/Turabian Style

Panagiotis G. Asteris; Anna Mamou; Mohsen Hajihassani; Mahdi Hasanipanah; Mohammadreza Koopialipoor; Tien-Thinh Le; Navid Kardani; Danial J. Armaghani. 2021. "Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks." Transportation Geotechnics 29, no. : 100588.

Original article
Published: 23 April 2021 in Neural Computing and Applications
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The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions. Hence, the present study investigates the applicability of other common machine learning techniques, i.e., support vector machine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. To this end, a big experimental database has been compiled based on experimental data available in the literature considering, namely the cement grade, which is an important parameter for the modeling of mortar’s CS. Other important parameters are namely the age, the water-to-binder ratio, the particle size distribution of the sand and the amount of plasticizer. Many models based on the influential factors affecting machine learning techniques have been developed, and their prediction capacities have been assessed using performance indexes. The present research highlights the potential of AdaBoost and RF models as useful tools which can assist in mortar design and/or optimization. In addition, mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on the compressive strength.

ACS Style

Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial J. Armaghani; Evgenios A. Kotsonis; Paulo B. Lourenço. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Computing and Applications 2021, 1 -33.

AMA Style

Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial J. Armaghani, Evgenios A. Kotsonis, Paulo B. Lourenço. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Computing and Applications. 2021; ():1-33.

Chicago/Turabian Style

Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial J. Armaghani; Evgenios A. Kotsonis; Paulo B. Lourenço. 2021. "Prediction of cement-based mortars compressive strength using machine learning techniques." Neural Computing and Applications , no. : 1-33.

Journal article
Published: 20 April 2021 in Applied Sciences
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This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

ACS Style

Jie Zeng; Panayiotis Roussis; Ahmed Mohammed; Chrysanthos Maraveas; Seyed Fatemi; Danial Armaghani; Panagiotis Asteris. Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences 2021, 11, 3705 .

AMA Style

Jie Zeng, Panayiotis Roussis, Ahmed Mohammed, Chrysanthos Maraveas, Seyed Fatemi, Danial Armaghani, Panagiotis Asteris. Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences. 2021; 11 (8):3705.

Chicago/Turabian Style

Jie Zeng; Panayiotis Roussis; Ahmed Mohammed; Chrysanthos Maraveas; Seyed Fatemi; Danial Armaghani; Panagiotis Asteris. 2021. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8: 3705.

Journal article
Published: 17 April 2021 in Cement and Concrete Research
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This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

ACS Style

Panagiotis G. Asteris; Athanasia D. Skentou; Abidhan Bardhan; Pijush Samui; Kypros Pilakoutas. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research 2021, 145, 106449 .

AMA Style

Panagiotis G. Asteris, Athanasia D. Skentou, Abidhan Bardhan, Pijush Samui, Kypros Pilakoutas. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research. 2021; 145 ():106449.

Chicago/Turabian Style

Panagiotis G. Asteris; Athanasia D. Skentou; Abidhan Bardhan; Pijush Samui; Kypros Pilakoutas. 2021. "Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models." Cement and Concrete Research 145, no. : 106449.

Journal article
Published: 03 March 2021 in Information
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The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

ACS Style

Iman Rahimi; Amir Gandomi; Panagiotis Asteris; Fang Chen. Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. Information 2021, 12, 109 .

AMA Style

Iman Rahimi, Amir Gandomi, Panagiotis Asteris, Fang Chen. Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. Information. 2021; 12 (3):109.

Chicago/Turabian Style

Iman Rahimi; Amir Gandomi; Panagiotis Asteris; Fang Chen. 2021. "Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases." Information 12, no. 3: 109.

Methodologies and application
Published: 25 February 2021 in Soft Computing
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Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method available in the literature, which can reliably predict their strength based on the mix components. This limitation is attributed to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques for predicting the compressive strength of mortars is investigated. Specifically, Levenberg–Marquardt, biogeography-based optimization, and invasive weed optimization algorithms are used for this purpose (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial intelligence techniques to approximate the compressive strength of mortars in a reliable and robust manner.

ACS Style

Panagiotis G. Asteris; Liborio Cavaleri; Hai-Bang Ly; Binh Thai Pham. Surrogate models for the compressive strength mapping of cement mortar materials. Soft Computing 2021, 25, 6347 -6372.

AMA Style

Panagiotis G. Asteris, Liborio Cavaleri, Hai-Bang Ly, Binh Thai Pham. Surrogate models for the compressive strength mapping of cement mortar materials. Soft Computing. 2021; 25 (8):6347-6372.

Chicago/Turabian Style

Panagiotis G. Asteris; Liborio Cavaleri; Hai-Bang Ly; Binh Thai Pham. 2021. "Surrogate models for the compressive strength mapping of cement mortar materials." Soft Computing 25, no. 8: 6347-6372.

Journal article
Published: 20 January 2021 in Applied Sciences
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Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

ACS Style

Jie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences 2021, 11, 908 .

AMA Style

Jie Zeng, Panagiotis G. Asteris, Anna P. Mamou, Ahmed Salih Mohammed, Emmanuil A. Golias, Danial Jahed Armaghani, Koohyar Faizi, Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences. 2021; 11 (3):908.

Chicago/Turabian Style

Jie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. 2021. "The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand." Applied Sciences 11, no. 3: 908.

Journal article
Published: 05 November 2020 in Journal of Earthquake Engineering
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ACS Style

M.F. Ferrotto; P.G. Asteris; L. Cavaleri. Strategies of Identification of a Base-Isolated Hospital Building by Coupled Quasi-Static and Snap-Back Tests. Journal of Earthquake Engineering 2020, 1 -29.

AMA Style

M.F. Ferrotto, P.G. Asteris, L. Cavaleri. Strategies of Identification of a Base-Isolated Hospital Building by Coupled Quasi-Static and Snap-Back Tests. Journal of Earthquake Engineering. 2020; ():1-29.

Chicago/Turabian Style

M.F. Ferrotto; P.G. Asteris; L. Cavaleri. 2020. "Strategies of Identification of a Base-Isolated Hospital Building by Coupled Quasi-Static and Snap-Back Tests." Journal of Earthquake Engineering , no. : 1-29.

Journal article
Published: 17 September 2020 in Soil Dynamics and Earthquake Engineering
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The present study aims to compare the performance of two machine learning techniques that can unveil the relationship between the input and target variables and predict the ground vibration (peak particle velocity, PPV) due to quarry blasting. To this end, a Random Forest (RF) model and a Bayesian Network (BN) model were developed. Before developing these models, and in order to illustrate the necessity of proposing new intelligent systems, a new empirical equation is proposed, using maximum charge per delay and distance from the blast-face. The results confirm that there is indeed a need to develop intelligent systems with more input parameters. Thus, a Feature Selection (FS) model is applied to decrease the dimensionality of data and remove the irrelevant data. The outputs of this technique set five parameters, hole depth, power factor, stemming, maximum charge per delay and distance from the blast-face, as the most important model inputs necessary to predict PPV. After constructing FS-BN and FS-RF models and comparing them under different conditions (i.e., computational cost, accuracy and robustness), it is found that the developed FS-RF model can be introduced as a new model in the field of blasting environmental issues. The accuracy level of the FS-RF model is quite high; 92.95% and 90.32% for the train and test stages, respectively, while 92.95% and 87.09% accuracy is calculated for train and test of the FS-BN model. Thus, both developed hybrid models can effectively unveil the relationships between the input and target variables.

ACS Style

Jian Zhou; Panagiotis G. Asteris; Danial Jahed Armaghani; Binh Thai Pham. Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics and Earthquake Engineering 2020, 139, 106390 .

AMA Style

Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani, Binh Thai Pham. Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics and Earthquake Engineering. 2020; 139 ():106390.

Chicago/Turabian Style

Jian Zhou; Panagiotis G. Asteris; Danial Jahed Armaghani; Binh Thai Pham. 2020. "Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models." Soil Dynamics and Earthquake Engineering 139, no. : 106390.

Journal article
Published: 03 September 2020 in Materials
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When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above‑mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS‑RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS‑RT, FS‑RF, and FS‑CART, could be applied to predicting SFRC flat slabs.

ACS Style

Shasha Lu; Mohammadreza Koopialipoor; Panagiotis G. Asteris; Maziyar Bahri; Danial Jahed Armaghani. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs. Materials 2020, 13, 3902 .

AMA Style

Shasha Lu, Mohammadreza Koopialipoor, Panagiotis G. Asteris, Maziyar Bahri, Danial Jahed Armaghani. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs. Materials. 2020; 13 (17):3902.

Chicago/Turabian Style

Shasha Lu; Mohammadreza Koopialipoor; Panagiotis G. Asteris; Maziyar Bahri; Danial Jahed Armaghani. 2020. "A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs." Materials 13, no. 17: 3902.

Research article
Published: 30 August 2020 in Advances in Civil Engineering
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The selection of seismic inputs for nonlinear dynamic analysis is widely debated, mainly focusing on the advantages and disadvantages provided by the choice of natural, simulated, or artificial records. This work proves the differences in the structural behavior of RC buildings when using accelerograms with different levels of stationarity. Initially, nonlinear response under three sets of accelerograms equivalent in terms of pseudo acceleration spectrum is evaluated and compared. Then, the results of incremental dynamic analyses are compared by the statistical point of view considering different levels of irregularity for the reference structure.

ACS Style

Marco Filippo Ferrotto; Francesco Basone; Panangiotis G. Asteris; Liborio Cavaleri. Artificial Ground Motions and Nonlinear Response of RC Structures. Advances in Civil Engineering 2020, 2020, 1 -14.

AMA Style

Marco Filippo Ferrotto, Francesco Basone, Panangiotis G. Asteris, Liborio Cavaleri. Artificial Ground Motions and Nonlinear Response of RC Structures. Advances in Civil Engineering. 2020; 2020 ():1-14.

Chicago/Turabian Style

Marco Filippo Ferrotto; Francesco Basone; Panangiotis G. Asteris; Liborio Cavaleri. 2020. "Artificial Ground Motions and Nonlinear Response of RC Structures." Advances in Civil Engineering 2020, no. : 1-14.

Original article
Published: 10 August 2020 in Neural Computing and Applications
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Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

ACS Style

Danial Jahed Armaghani; Panagiotis G. Asteris. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Computing and Applications 2020, 33, 4501 -4532.

AMA Style

Danial Jahed Armaghani, Panagiotis G. Asteris. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Computing and Applications. 2020; 33 (9):4501-4532.

Chicago/Turabian Style

Danial Jahed Armaghani; Panagiotis G. Asteris. 2020. "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength." Neural Computing and Applications 33, no. 9: 4501-4532.

Journal article
Published: 06 August 2020 in Journal of Building Engineering
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This study aims to propose a multi-strut macro-model, capable of simulating the overall force-displacement behaviour of infilled frames with various opening configurations. For this purpose, the results of finite element modelling calibrated against several experimental data are employed to determine the characteristics of a multiple-strut model for such infilled frames. The results indicate that the size of the opening along with its position, compared to the size of the infill wall, can significantly affect both the inclination and also the effective width of the struts and therefore, the overall behaviour of infilled frames with opening. The proposed model is evaluated parametrically against FEM numerical results, with varying characteristics such as opening size and position, opening height-to-length ratio, height-to-length ratio of the infilled frame and relative rigidity of frame to the infill wall. The comparison of the derived results with the analytical and experimental findings demonstrates the ability of the model to approximate the lateral response of infilled frames with openings in a reliable and robust manner. A simple reduction factor for the ultimate strength of the perforated infilled frames is proposed based on opening size relative to infill wall size as well as relative stiffness of the frame and infill wall.

ACS Style

Mohammad Yekrangnia; Panagiotis G. Asteris. Multi-strut macro-model for masonry infilled frames with openings. Journal of Building Engineering 2020, 32, 101683 .

AMA Style

Mohammad Yekrangnia, Panagiotis G. Asteris. Multi-strut macro-model for masonry infilled frames with openings. Journal of Building Engineering. 2020; 32 ():101683.

Chicago/Turabian Style

Mohammad Yekrangnia; Panagiotis G. Asteris. 2020. "Multi-strut macro-model for masonry infilled frames with openings." Journal of Building Engineering 32, no. : 101683.

Original article
Published: 25 July 2020 in Neural Computing and Applications
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The main objective of the present work is to estimate the load-carrying capacity of concrete-filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) algorithms. In particular, the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic optimization methods, such as the biogeography-based optimization (ANFIS-BBO), the particle swarm optimization (ANFIS-PSO), and the genetic algorithm (ANFIS-GA), have been employed taking into account the variability of input parameters. Commonly used statistical criteria, such as the coefficient of determination (R2), the a20-index, and the root mean squared error (RMSE), were utilized to evaluate and compare the effectiveness of the proposed AI models. The Monte Carlo approach was used to propagate the variability in the input space to the predicted output. The results showed that the ANFIS system, optimized by PSO, was the most effective and robust model with respect to three considered criteria (a20-index = 0.881, R2 = 0.942 and RMSE = 185.631). Sensitivity analysis was performed, indicating that the minor axis length and thickness of the steel tube exhibited the highest contribution to the axial compression load-carrying capacity of the CFST.

ACS Style

Hai-Bang Ly; Binh Thai Pham; Lu Minh Le; Tien-Thinh Le; Vuong Minh Le; Panagiotis G. Asteris. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing and Applications 2020, 33, 3437 -3458.

AMA Style

Hai-Bang Ly, Binh Thai Pham, Lu Minh Le, Tien-Thinh Le, Vuong Minh Le, Panagiotis G. Asteris. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing and Applications. 2020; 33 (8):3437-3458.

Chicago/Turabian Style

Hai-Bang Ly; Binh Thai Pham; Lu Minh Le; Tien-Thinh Le; Vuong Minh Le; Panagiotis G. Asteris. 2020. "Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models." Neural Computing and Applications 33, no. 8: 3437-3458.