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Journal ArticleDOI: 10.1007/S00366-021-01329-3

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

02 Mar 2021-Engineering With Computers (Springer London)-pp 1-20
Abstract: Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.

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Journal ArticleDOI: 10.1016/J.CEMCONRES.2021.106449
Abstract: 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.

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23 Citations


Journal ArticleDOI: 10.1016/J.ASOC.2021.107595
Abidhan Bardhan1, Pijush Samui1, Kuntal Ghosh2, Amir H. Gandomi3  +1 moreInstitutions (3)
Abstract: This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.

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12 Citations


Journal ArticleDOI: 10.1016/J.ENGGEO.2021.106239
Abidhan Bardhan1, Candan Gokceoglu2, A. Burman1, Pijush Samui1  +1 moreInstitutions (3)
Abstract: California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects.

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12 Citations


Journal ArticleDOI: 10.1007/S12046-021-01640-1
Abstract: Earthquake-induced liquefaction is an unpredicted phenomenon that causes catastrophic damages and devastation to the environment, structures, and human life. The assessment of soil liquefaction behavior is a decisive work for geotechnical engineers especially during the designing phase of any civil engineering projects. These decisions implicate tedious and costly experimental procedures and extensive evaluation. Considering these facts, the present study aims to simplify the process of evaluating soil’s liquefaction behavior in a broader domain involving the least experimental datasets. Three PCA (principal component analysis)-based advanced hybrid computational models, namely PCA-ANN, PCA-ANFIS, and PCA-ELM were developed to predict the liquefaction behavior of soils. The dimension reduction technique, i.e. PCA, was used to avoid the multicollinearity effect during the course of the development of the said models. Geotechnical parameters, namely plasticity index, SPT blow count, water content to liquid limit ratio, bulk density, total stress, effective stress, and fine content along with other seismic input variables, such as the ratio of peak ground acceleration and acceleration due to gravity, and magnitude of an earthquake were used to develop the predictive models. The predictive accuracy of the proposed models was evaluated via several fitness parameters. In the end, the best predictive model was determined using a novel tool called Rank Analysis. Based on the results, it has been established that the PCA-ELM hybrid computational model can be considered as a new alternative tool to assist geotechnical engineers in the task of assessing the liquefaction potential of soil during the preliminary design stage in any engineering project.

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Topics: Liquefaction (60%), Soil liquefaction (56%), Effective stress (50%)

8 Citations


Journal ArticleDOI: 10.1016/J.RSER.2021.111315
Abstract: Accurate photovoltaic (PV) power prediction is necessary for future development of the micro-grids projects and the economic dispatch sector. This study investigates the potential of using a novel hybrid approach of adaptive swarm intelligence techniques and adaptive network-based fuzzy inference system (ANFIS) in estimating the PV power of a solar system at different time horizons, from 0 to 24 h. The developed approach is an integration of ANFIS and particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients, i.e., ANFIS-APSO (ANFIS-PSO with adaptive acceleration coefficients) and ANFIS-IPSO (ANFIS-PSO with time-varying acceleration coefficients), were developed. The performance of the proposed models was compared with other hybrid ANFIS models, namely ANFIS-PSO (ANFIS coupled with PSO), ANFIS-BBO (ANFIS coupled with biogeography-based optimization), ANFIS-GA (ANFIS coupled with genetic algorithm), and ANFIS-GWO (ANFIS coupled with grey wolf optimization). For this purpose, the climatic variables and historical PV power data of a 960 kWP grid-connected PV system in the south of Italy were used to design and evaluate the models. Several statistical analyses were implemented to evaluate the accuracy of the proposed models and assess the impact of variables that affects the PV power values. The experimental results show that the proposed ANFIS-APSO attained the most accurate prediction of the PV power with R2 = 0.835 and 0.657, RMSE = 0.088 kW and 0.081 kW, and MAE = 0.077 kW and 0.079 kW in the testing phase at time horizons 12 h and 24 h, respectively. Based on the obtained results, the newly constructed ANFIS-APSO outperformed the standard ANFIS-PSO model including other hybrid models, and hence very potential to be a new alternative to assist engineers for predicting the PV power of solar systems at short- and long-time horizons.

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8 Citations


References
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Journal ArticleDOI: 10.1029/2000JD900719
Abstract: A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model-simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.

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Topics: Diagram (54%), Statistical model (52%)

4,471 Citations


Journal ArticleDOI: 10.1111/J.0006-341X.2001.00120.X
Wei Pan1Institutions (1)
01 Mar 2001-Biometrics
Abstract: Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.

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1,990 Citations


Open accessPosted Content
Xin-She Yang1Institutions (1)
Abstract: Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality, and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

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Topics: Firefly algorithm (67%), Metaheuristic (57%), Optimization problem (54%)

1,628 Citations


Open accessJournal ArticleDOI: 10.1504/IJBIC.2010.032124
Xin-She Yang1Institutions (1)
Abstract: Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

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Topics: Firefly algorithm (66%), Metaheuristic (57%)

1,582 Citations


Journal ArticleDOI: 10.1680/GEOT.2006.56.2.81
01 Mar 2006-Geotechnique
Abstract: Energy foundations and other thermo-active ground structures, energy wells, and pavement heating represent an innovative technology that contributes to environmental protection and provides substan...

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801 Citations