Applied Sciences

Research

20 pages, 2907 KiB

Open AccessArticle

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

by Diyuan Li, Mohammad Reza Moghaddam, Masoud Monjezi, Danial Jahed Armaghani and Amirhossein Mehrdanesh

Appl. Sci. 2020, 10(7), 2364; https://doi.org/10.3390/app10072364 - 30 Mar 2020

Cited by 24 | Viewed by 5196

Abstract

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron [...] Read more.

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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23 pages, 3259 KiB

Open AccessArticle

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

by Dongchun Tang, Behrouz Gordan, Mohammadreza Koopialipoor, Danial Jahed Armaghani, Reza Tarinejad, Binh Thai Pham and Van Van Huynh

Appl. Sci. 2020, 10(5), 1761; https://doi.org/10.3390/app10051761 - 4 Mar 2020

Cited by 32 | Viewed by 4460

Abstract

Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem [...] Read more.

Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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17 pages, 6756 KiB

Open AccessArticle

Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

by Deliang Sun, Mahshid Lonbani, Behnam Askarian, Danial Jahed Armaghani, Reza Tarinejad, Binh Thai Pham and Van Van Huynh

Appl. Sci. 2020, 10(5), 1691; https://doi.org/10.3390/app10051691 - 2 Mar 2020

Cited by 38 | Viewed by 4125

Abstract

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine [...] Read more.

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R²) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R² of 0.807 and ANN model with R² of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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15 pages, 12105 KiB

Open AccessArticle

Developing Hybrid Machine Learning Models for Estimating the Unconfined Compressive Strength of Jet Grouting Composite: A Comparative Study

by Yuantian Sun, Guichen Li and Junfei Zhang

Appl. Sci. 2020, 10(5), 1612; https://doi.org/10.3390/app10051612 - 28 Feb 2020

Cited by 36 | Viewed by 4073

Abstract

Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is [...] Read more.

Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is required to elucidate the unknown nonlinear relationship between the UCS and the influencing variables. In this study, six computational intelligence techniques using machine learning (ML) algorithms were used to develop the mathematical models, which includes back-propagation neural network (BPNN), random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR). In addition, the hyper-parameters in these typical algorithms (e.g., the hidden layers in BPNN, the gamma in SVM, and the number of neighbor samples in KNN) were tuned by the recently developed beetle antennae search algorithm (BAS). To prepare the dataset for these ML models, three types of cementitious grout and three types of chemical grout were mixed with coal powders extracted from the Guobei coalmine, Anhui Province, China to create coal-grout composites. In total, 405 coal-grout specimens in total were extracted and tested. Several variables such as grout types, coal-grout ratio, and curing time were chosen as input parameters, while UCS was the output of these models. The results show that coal-chemical grout composites had higher strength in the short-term, while the coal-cementitious grout composites could achieve stable and high strength in the long term. BPNN, DT, and SVM outperform the others in terms of predicting the UCS of the coal-grout composites. The outstanding performance of the optimum ML algorithms for strength prediction facilitates JG parameter design in practice and could be the benchmark for the wider application of ML methods in JG engineering for coal improvement. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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23 pages, 5673 KiB

Open AccessArticle

Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

by Mingxiang Cai, Mohammadreza Koopialipoor, Danial Jahed Armaghani and Binh Thai Pham

Appl. Sci. 2020, 10(4), 1486; https://doi.org/10.3390/app10041486 - 21 Feb 2020

Cited by 42 | Viewed by 3885

Abstract

Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the [...] Read more.

Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the first phase, 103 real cases of deformation in earth dams were collected and analyzed due to earthquakes that occurred over recent years. Using nonlinear and machine learning techniques, i.e., group method of data handling (GMDH) and multivariate adaptive regression splines (MARS), two models for prediction of the slope deformation in earth dams under the various types of earthquakes were applied and developed. The main parameters used in these simulation techniques were earthquake magnitude (Mw), fundamental period ratio (Td/Tp), yield acceleration ratio (ay/amax) as inputs and value of slope deformation (Dave) as output. Finally, in order to check the accuracy of the results of the new models, a comparison was made with the previous relations and models in seismic conditions for the slope deformation in earth dams. The results showed that the MARS model, which is able to provide a mathematical equation, has a better result than the GMDH model. These new models are recommended to be used for future analyses based on their flexible capabilities. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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17 pages, 3166 KiB

Open AccessArticle

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

by Zhi Yu, Xiuzhi Shi, Jian Zhou, Xin Chen and Xianyang Qiu

Appl. Sci. 2020, 10(4), 1403; https://doi.org/10.3390/app10041403 - 19 Feb 2020

Cited by 68 | Viewed by 5193

Abstract

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control [...] Read more.

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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18 pages, 2346 KiB

Open AccessArticle

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

by Hong Zhang, Jian Zhou, Danial Jahed Armaghani, M. M. Tahir, Binh Thai Pham and Van Van Huynh

Appl. Sci. 2020, 10(3), 869; https://doi.org/10.3390/app10030869 - 27 Jan 2020

Cited by 88 | Viewed by 5712

Abstract

In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak [...] Read more.

In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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17 pages, 3131 KiB

Open AccessArticle

Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

by Amir Mahdiyar, Danial Jahed Armaghani, Mohammadreza Koopialipoor, Ahmadreza Hedayat, Arham Abdullah and Khairulzan Yahya

Appl. Sci. 2020, 10(2), 472; https://doi.org/10.3390/app10020472 - 9 Jan 2020

Cited by 51 | Viewed by 4924

Abstract

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an [...] Read more.

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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20 pages, 3400 KiB

Open AccessArticle

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

by Guichen Li, Deepak Kumar, Pijush Samui, Hima Nikafshan Rad, Bishwajit Roy and Mahdi Hasanipanah

Appl. Sci. 2020, 10(2), 434; https://doi.org/10.3390/app10020434 - 7 Jan 2020

Cited by 21 | Viewed by 3156

Abstract

Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and [...] Read more.

Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R²), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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21 pages, 5822 KiB

Open AccessArticle

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

by Hai-Bang Ly, Tien-Thinh Le, Lu Minh Le, Van Quan Tran, Vuong Minh Le, Huong-Lan Thi Vu, Quang Hung Nguyen and Binh Thai Pham

Appl. Sci. 2019, 9(24), 5458; https://doi.org/10.3390/app9245458 - 12 Dec 2019

Cited by 56 | Viewed by 5769

Abstract

The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) [...] Read more.

The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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18 pages, 2400 KiB

Open AccessArticle

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

by Lei Huang, Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani and M. M. Tahir

Appl. Sci. 2019, 9(24), 5372; https://doi.org/10.3390/app9245372 - 9 Dec 2019

Cited by 102 | Viewed by 4269

Abstract

In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is [...] Read more.

In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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18 pages, 4011 KiB

Open AccessArticle

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

by Pijush Samui, Nhat-Duc Hoang, Viet-Ha Nhu, My-Linh Nguyen, Phuong Thao Thi Ngo and Dieu Tien Bui

Appl. Sci. 2019, 9(22), 4912; https://doi.org/10.3390/app9224912 - 15 Nov 2019

Cited by 15 | Viewed by 3293

Abstract

In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic [...] Read more.

In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R²) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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20 pages, 4374 KiB

Open AccessArticle

A Novel Artificial Intelligence Technique to Estimate the Gross Calorific Value of Coal Based on Meta-Heuristic and Support Vector Regression Algorithms

by Hoang-Bac Bui, Hoang Nguyen, Yosoon Choi, Xuan-Nam Bui, Trung Nguyen-Thoi and Yousef Zandi

Appl. Sci. 2019, 9(22), 4868; https://doi.org/10.3390/app9224868 - 14 Nov 2019

Cited by 35 | Viewed by 4360

Abstract

Gross calorific value (GCV) is one of the essential parameters for evaluating coal quality. Therefore, accurate GCV prediction is one of the primary ways to improve heating value as well as coal production. A novel evolutionary-based predictive system was proposed in this study [...] Read more.

Gross calorific value (GCV) is one of the essential parameters for evaluating coal quality. Therefore, accurate GCV prediction is one of the primary ways to improve heating value as well as coal production. A novel evolutionary-based predictive system was proposed in this study for predicting GCV with high accuracy, namely the particle swarm optimization (PSO)-support vector regression (SVR) model. It was developed based on the SVR and PSO algorithms. Three different kernel functions were employed to establish the PSO-SVR models, including radial basis function, linear, and polynomial functions. Besides, three benchmark machine learning models including classification and regression trees (CART), multiple linear regression (MLR), and principle component analysis (PCA) were also developed to estimate GCV and then compared with the proposed PSO-SVR model; 2583 coal samples were used to analyze the proximate components and GCV for this study. Then, they were used to develop the mentioned models as well as check their performance in experimental results. Root-mean-squared error (RMSE), correlation coefficient (R²), ranking, and intensity color criteria were used and computed to evaluate the GCV predictive models developed. The results revealed that the proposed PSO-SVR model with radial basis function had better accuracy than the other models. The PSO algorithm was optimized in the SVR model with high efficiency. These should be used as a supporting tool in practical engineering to determine the heating value of coal seams in complex geological conditions. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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18 pages, 3791 KiB

Open AccessArticle

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

by Hoang-Long Nguyen, Binh Thai Pham, Le Hoang Son, Nguyen Trung Thang, Hai-Bang Ly, Tien-Thinh Le, Lanh Si Ho, Thanh-Hai Le and Dieu Tien Bui

Appl. Sci. 2019, 9(21), 4715; https://doi.org/10.3390/app9214715 - 5 Nov 2019

Cited by 58 | Viewed by 5299

Abstract

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the [...] Read more.

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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22 pages, 4422 KiB

Open AccessFeature PaperArticle

A Gene Expression Programming Model for Predicting Tunnel Convergence

by Mohsen Hajihassani, Shahrum Shah Abdullah, Panagiotis G. Asteris and Danial Jahed Armaghani

Appl. Sci. 2019, 9(21), 4650; https://doi.org/10.3390/app9214650 - 1 Nov 2019

Cited by 93 | Viewed by 5246

Abstract

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many [...] Read more.

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson’s ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model’s output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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15 pages, 6573 KiB

Open AccessArticle

Seismological and Engineering Demand Misfits for Evaluating Simulated Ground Motion Records

by Shaghayegh Karimzadeh

Appl. Sci. 2019, 9(21), 4497; https://doi.org/10.3390/app9214497 - 23 Oct 2019

Cited by 14 | Viewed by 3098

Abstract

Simulated ground motions have recently gained more attention in seismology and earthquake engineering. Since different characteristics of waveforms are expected to influence alternative structural response parameters, evaluation of simulations, for key components of seismological and engineering points of view is necessary. When seismological [...] Read more.

Simulated ground motions have recently gained more attention in seismology and earthquake engineering. Since different characteristics of waveforms are expected to influence alternative structural response parameters, evaluation of simulations, for key components of seismological and engineering points of view is necessary. When seismological aspect is of concern, consideration of a representative set of ground motion parameters is imperative. Besides, to test the applicability of simulations in earthquake engineering, structural demand parameters should simultaneously cover a descriptive set. Herein, simulations are evaluated through comparison of seismological against engineering misfits, individually defined in terms of log-scale misfit and goodness-of-fit score. For numerical investigations, stochastically simulated records of three earthquakes are considered: The 1992 Erzincan-Turkey, 1999 Duzce-Turkey and 2009 L’Aquila-Italy events. For misfit evaluation, seismological parameters include amplitude, duration and frequency content, while engineering parameters contain spectral acceleration, velocity and seismic input energy. Overall, the same trend between both misfits is observed. All misfits for Erzincan and Duzce located on basins are larger than those corresponding to L’Aquila mostly placed on stiff sites. The engineering misfits, particularly in terms of input energy measures, are larger than seismological misfits. In summary, the proposed misfit evaluation methodology seems useful to evaluate simulations for engineering practice. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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24 pages, 8573 KiB

Open AccessArticle

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

by Mohammad Ehteram, Samira Ghotbi, Ozgur Kisi, Ali Najah Ahmed, Gasim Hayder, Chow Ming Fai, Mathivanan Krishnan, Haitham Abdulmohsin Afan and Ahmed EL-Shafie

Appl. Sci. 2019, 9(19), 4149; https://doi.org/10.3390/app9194149 - 3 Oct 2019

Cited by 35 | Viewed by 3892

Abstract

Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving [...] Read more.

Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS–BA, ANFIS–WA, MFNN–BA, and MFNN–WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash–Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS–BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0. 75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS–BA had more reliable performance compared to other models. Thus, the ANFIS–BA model has high potential for predicting SSL. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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16 pages, 1591 KiB

Open AccessArticle

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

by Hai-Bang Ly, Binh Thai Pham, Dong Van Dao, Vuong Minh Le, Lu Minh Le and Tien-Thinh Le

Appl. Sci. 2019, 9(18), 3841; https://doi.org/10.3390/app9183841 - 12 Sep 2019

Cited by 90 | Viewed by 6088

Abstract

Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy [...] Read more.

Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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19 pages, 2749 KiB

Open AccessArticle

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

by Hai Xu, Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani and Mahmood Md Tahir

Appl. Sci. 2019, 9(18), 3715; https://doi.org/10.3390/app9183715 - 6 Sep 2019

Cited by 183 | Viewed by 9546

Abstract

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of [...] Read more.

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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20 pages, 6646 KiB

Open AccessArticle

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

by Eunseo Oh and Hyunsoo Lee

Appl. Sci. 2019, 9(18), 3646; https://doi.org/10.3390/app9183646 - 4 Sep 2019

Cited by 7 | Viewed by 4653

Abstract

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a [...] Read more.

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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13 pages, 2047 KiB

Open AccessArticle

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

by Mosbeh R. Kaloop, Deepak Kumar, Pijush Samui, Alaa R. Gabr, Jong Wan Hu, Xinghan Jin and Bishwajit Roy

Appl. Sci. 2019, 9(16), 3221; https://doi.org/10.3390/app9163221 - 7 Aug 2019

Cited by 71 | Viewed by 6597

Abstract

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (M_r) under wet-dry cycle conditions [...] Read more.

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (M_r) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r²) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted M_r values generally yielded the same distribution and trend as the observed M_r data. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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17 pages, 3329 KiB

Open AccessArticle

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

by Guirong Dong, Xiaozhe Wang and Dianzi Liu

Appl. Sci. 2019, 9(15), 2979; https://doi.org/10.3390/app9152979 - 25 Jul 2019

Cited by 4 | Viewed by 3708

Abstract

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific [...] Read more.

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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24 pages, 6935 KiB

Open AccessArticle

Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

by Phong Tung Nguyen, Tran Thi Tuyen, Ataollah Shirzadi, Binh Thai Pham, Himan Shahabi, Ebrahim Omidvar, Ata Amini, Hersh Entezami, Indra Prakash, Tran Van Phong, Thao Ba Vu, Tran Thanh, Lee Saro and Dieu Tien Bui

Appl. Sci. 2019, 9(14), 2824; https://doi.org/10.3390/app9142824 - 15 Jul 2019

Cited by 59 | Viewed by 5736

Abstract

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) [...] Read more.

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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23 pages, 12625 KiB

Open AccessArticle

Estimating PM₁₀ Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

by Xuan-Nam Bui, Chang Woo Lee, Hoang Nguyen, Hoang-Bac Bui, Nguyen Quoc Long, Qui-Thao Le, Van-Duc Nguyen, Ngoc-Bich Nguyen and Hossein Moayedi

Appl. Sci. 2019, 9(14), 2806; https://doi.org/10.3390/app9142806 - 12 Jul 2019

Cited by 42 | Viewed by 4877

Abstract

Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM₁₀. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM₁₀ in open-pit mines. Therefore, the [...] Read more.

Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM₁₀. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM₁₀ in open-pit mines. Therefore, the prediction and control of PM₁₀ concentration in the production process are necessary for environmental and health protection. In this study, PM₁₀ concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM₁₀ concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM₁₀ concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R²) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R² of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R² of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R² of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM₁₀ concentration caused by drilling operations in open-pit mines. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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23 pages, 9611 KiB

Open AccessArticle

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

by Le Thi Le, Hoang Nguyen, Jie Dou and Jian Zhou

Appl. Sci. 2019, 9(13), 2630; https://doi.org/10.3390/app9132630 - 28 Jun 2019

Cited by 265 | Viewed by 10242

Abstract

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of [...] Read more.

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R²), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R² of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R² of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively. Full article

(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

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