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Frontiers of Structural and Civil Engineering 2020年第2期14卷 487-500页

作者： Lingyun YOU Kezhen YAN Nengyuan LIU College of Civil Engineering Hunan UniversityChangsha 410082China Department of Civil and Environmental Engineering Michigan Technological UniversitHoughtonMI 49931USA

The objective of this study is to evaluate the performance of the artificial neural network(ANN)approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure.To achieve this goal,two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure.The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to flling weight deflectometer load.In addition,two proposed ANN models were verifed by comparing the results of ANN models with the results of PADAL and double multiple regression models.The measured pavement deflection basin data was used for the verifications.The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models.PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous.The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions.In addition,the back-calculation model avoided the back-calculation errors by considering the interlayer condition,which was barely considered by former models reported in the published studies.

关键词： asphalt pavement interlayer conditions finite element method artificial neural network back-calculation

Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models

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Journal of University of Science and Technology Beijing 2005年第5期12卷 410-415页

作者： Weihong Xu Wei You Yaxiu Liu Bingzhe Bai Hongsheng Fang Department of Mechanical and Electrical Engineering North China Institute of Science and Technology Beijing 101601 China Department of Electronics and Information Engineering North China Institute of Science and Technology Beijing 101601 China Department of Materials Science and Engineering Tsinghua University Beijing 100084 China

The quantitative effect of Ni content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels was analyzed using artificial neural network models. The results showed that Ni may retard the high- and medium-temperature transformation and martensite transformation. The results conform to the materials science theories.

关键词： novel air-cooled bainite steels nickel CCT diagrams artificial neural network

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

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Frontiers of Structural and Civil Engineering 2017年第1期11卷 90-99页

作者： Faezehossadat KHADEMI Mahmoud AKBARI Sayed Mohammadmehdi JAMAL Mehdi NIKOO Department of Civil Architectural and Environmental Engineering Illinois Institute of Technology Chicago IL 60616 USA Civil Engineering Department University of Kashan Kashan *** Iran Department of Civil Engineering University of Hormozgan Bandar Abbas 3995 IRAN Ahvaz Branch Islamic Azad University Ahvaz *** IRAN

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model （MLR）, artificial neural network （ANN）, and adaptive neuro-fuzzy inference system （ANFIS） are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis （SA） for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis （SA）

Operation Optimization of Liquid Cooling Systems in Data Centers by the Heat Current Method and artificial neural network

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Journal of Thermal Science 2020年第4期29卷 1063-1075页

作者： SHAO Wei CHEN Qun HE Kelun ZHANG Mengqi Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Thermal Science and Technology Shandong UniversityJi’nan 250061China

Liquid cooling systems in data centers have been attracting more attentions due to its better cooling capability and less energy consumption. In order to propose an effective optimization method for the operation of indirect liquid cooling systems, this paper first constructs an experiment platform and applies the heat current method to build the global heat transfer constraints of the whole system. Particularly, the thermal conductance of each heat exchanger under different working conditions is predicted by the artificial neural networks(ANN) trained by the historical data. On this basis, combining the heat transfer and fluid flow constraints together with the Lagrange multiplier method builds the optimization model with the objective of minimum pumping power consumption(PPC), solving which by the solution strategy designed obtains the optimal frequencies of the variable frequency pumps(VFPs). Operating with the optimal and other feasible operating conditions validates the optimization model. Meanwhile, the experiments with variable heat loads and flow resistances provide some guidelines for the optimal system operation. For instance, to address heat load increase of a branch, it needs to increase the frequencies of the VFPs, not only the corresponding hot loop but also the whole cold loop.

关键词： liquid cooling system energy conservation heat current method global heat transfer constraints artificial neural network

Prediction of bed load sediments using different artificial neural network models

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Frontiers of Structural and Civil Engineering 2020年第2期14卷 374-386页

作者： Reza ASHEGHI Seyed Abbas HOSSEINI Department of Civil Engineering Science and Research BranchIslamic Azad UniversityTehranIran

Modeling and prediction of bed loads is an important but difficult issue in river engineering.The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data.In this paper,three different artificial neural networks(ANNs)including multilayer percepterons,radial based function(RBF),and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed.The optimum models were found through 102 data sets of flow discharge,flow velocity,water surface slopes,flow depth,and mean grain size.The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data.The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models.The accuracy performance of all models was evaluated and interpreted using different statistical error criteria,analytical graphs and confusion matrixes.Although the ANN models predicted compatible outputs but the RBF with 79%correct classification rate corresponding to 0.191 nctwork error was outperform than others.

关键词： bed load prediction artificial neural network modeling empirical equations

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

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International Journal of Coal Science & Technology 2021年第4期8卷 737-755页

作者： Amit Kumar Gorai Simit Raval Ashok Kumar Patel Snehamoy Chatterjee Tarini Gautam Department of Mining Engineering National Institute of TechnologyRourkela 769008India School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Department of Computer Science&Engineering C.V.Raman Global UniversityBhubaneswarOdisha 752054India Department of Geological and Mining Engineering and Science Michigan Technological UniversityHoughtonMI 49931USA

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.

关键词： Coal characterization Machine vision system artificial neural network Gaussian process regression

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine

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Friction 2020年第6期8卷 1102-1116页

作者： Mustafa ULAS Osman ALTAY Turan GURGENC Cihan OZEL Department of the Software Engineering Firat UniversityElazig 23119Turkey Department of the Automotive Engineering Firat UniversityElazig 23119Turkey Department of the Mechanical Engineering Firat UniversityElazig 23119Turkey

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.

关键词： wear loss prediction surface coating plasma transferred arc welding artificial neural network extreme learning machine

Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability

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Journal of Central South University 2015年第5期22卷 1878-1891页

作者： Hamed Farshbaf Aghajani Hossein Salehzadeh Habib Shahnazari School of Civil Engineering Iran University of Science and Technology

The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking. Thus, this work aims to develop a procedure for connecting the sand friction angle and the loading orientation. All principal stress rotation tests in the literatures were processed via an artificial neural network. Then, with sensitivity analysis, the effect of intrinsic soil properties,consolidation history, and test sample characteristics on enhancing anisotropy was examined. The results imply that decreasing the grain size of the soil increases the effect of anisotropy on soil shear strength. In addition, increasing the angularity of grains increases the anisotropy effect in the sample. The stability of a sandy slope was also examined by considering the anisotropy in shear strength parameters. If the anisotropy effect is neglected, slope safety is overestimated by 5%-25%. This deviation is more apparent in flatter slopes than in steeper ones. However, the critical slip surface in the most slopes is the same in isotropic and anisotropic conditions.

关键词： anisotropy artificial neural network sand principal stress rotation slope stability

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power

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Frontiers of Structural and Civil Engineering 2020年第6期14卷 1299-1315页

作者： Fangvu LIU Wenqi DING Yafei QIAO Linbing WANG Department of Civil and Environmental Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China Key Laboratory of Geotechnical and Underground Engineering(Tongji University) Ministry of EducationShanghai 200092China

The tensile behavior of hybrid fiber reinforced concrete(HFRC)is important to the design of HFRC and HFRC structure.This study used an artificial neural network(ANN)model to describe the tensile behavior of HFRC.This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC.In the model,three methods to process output features(no-processed,mid-processed,and processed)are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data.This means the strain should be normalized while the stress doesn’t need normalization.To prepare the database of the model,both many direct tensile test results and the relevant literature data are collected.Moreover,a traditional equation-based model is also established and compared with the ANN model.The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve,tensile strength,and strain corresponding to tensile strength of HFRC.Finally,the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength.The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength,while steel fibers tend to make more contributions to these two items than PVA fibers.

关键词： artificial neural network hybrid fiber reinforced concrete tensile behavior sensitivity analysis stress-strain

Research and applications of artificial neural network in pavement engineering:A state-of-the-art review

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Journal of Traffic and Transportation Engineering(English Edition) 2021年第6期8卷 1000-1021页

作者： Xu Yang Jinchao Guan Ling Ding Zhanping You Vincent C.S.Lee Mohd Rosli Mohd Hasan Xiaoyun Cheng School of Highway Chang'an UniversityXi'an 710064China School of Future Transportation Chang'an UniversityXi'an 710064China College of Transportation Engineering Chang'an UniversityXi'an 710064China Department of Civil and Environmental Engineering Michigan Technological UniversityHoughtonMI 49931USA Faculty of Information Technology Monash UniversityClaytonVIC 3800Australia School of Civil Engineering Universiti Sains MalaysiaNibong Tebal 14300Pulau PinangMalaysia

Given the great advancements in soft computing and data science,artificial neural network(ANN)has been explored and applied to handle complicated problems in the field of pavement engineering.This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering,including pavement design,construction,inspection and monitoring,and maintenance.This study focused on the papers published over the last three decades,especially the studies conducted since 2013.Through literature retrieval,a total of 683 papers in this field were identified,among which 143 papers were selected for an in-depth review.The ANN architectures used in these studies mainly included multi-layer perceptron neural network(MLPNN),convolutional neural network(CNN)and recurrent neural network(RNN)for processing one-dimensional data,two-dimensional data and time-series data.CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor.While ANN has been proved to be an effective tool for pavement material design,cost analysis,defect detection and maintenance planning,it is facing huge challenges in terms of data collection,parameter optimization,model transferability and low-cost data annotation.More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities.

关键词： Pavement engineering Pavement design artificial neural network Deep learning Pavement life cycle Health inspection and monitoring