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Study on Dynamic Modulus Prediction Model of In-Service Asphalt Pavement

BUILDINGS 2024年第8期14卷 2550页

作者： Wang, Duanyi Luo, Chuanxi Li, Jian He, Jun South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510000 Peoples R China Xiaoning Inst Roadway Engn Guangzhou 510000 Peoples R China

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement's dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements.

关键词： in-service asphalt pavement dynamic modulus prediction model modulus back-calculation gene expression programming

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On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Amirteimoori, Alireza Allahviranloo, Tofigh Zadmirzaei, Majid Hasanzadeh, Fahimeh Istinye Univ Fac Engn & Nat Sci Istanbul Turkiye Univ Guilan Fac Nat Resources Dept Forestry Rasht Iran Lorestan Univ Dept Math & Comp Sci Khorramabad Iran

Greenhouse gases (GHG) remain in the atmosphere for a very long-time causing alarmingly fast warming worldwide (global warming);especially Carbon dioxide (CO2) emissions have become a worldwide concern because of their harmful effects on the climate, and they are considered as an undesirable product of a lot of production systems. Various models dealing with undesirable outputs for measuring environmental efficiency have been employed to control greenhouse gas emissions via forecasting and/or optimizing their emissions. In this regard, this study proposes a novel modified Fuzzy Undesirable Non-discretionary DEA (FUNDEA) model to Measure environmental efficiency, and combine it with some novel artificial intelligence algorithms (Artificial Neural Network (ANN), gene expression programming (GEP) and Artificial Immune System (AIS)) in order to predict optimal values of inefficient Decision-Making Units (DMUs) for being more efficient and mitigating their Co2 emissions in the uncertain environment for the first time herein. The model is applied to a dataset of 24 Iranian forest management units. Although our findings show that 17 DMUs are inefficient with a weak efficiency dispersion, these inefficient DMUs could improve their efficiency border by following the combined approaches (FUNDEA-ANN, FUNDEA-GEP and FUNDEA-AIS). As a consequence, the applied FUNDEA- artificial intelligent approaches are performed very well in predicting the optimal values of CO2 emissions and, hence increasing the total environmental efficiency.

关键词： Data envelopment analysis Environmental efficiency Artificial neural network gene expression programming Artificial immune system

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Developing a sustainable concrete incorporating bentonite clay and silica fume: Mechanical and durability performance

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JOURNAL OF CLEANER PRODUCTION 2022年第0期337卷 130315-130315页

作者： Ashraf, Muhammad Iqbal, Muhammad Farjad Rauf, Momina Ashraf, Muhammad Usman Ulhaq, Aman Muhammad, Hamid Liu, Qing-feng Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China GIK Inst Engn Sci & Technol Dept Civil Engn Swabi 23460 Pakistan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Islamabad Pakistan NUST Dept Civil Engn Mil Coll Engn Risalpur Pakistan Shanghai Key Lab Digital Maintenance Bldg & Shanghai Peoples R China

The use of supplementary cementitious materials (SCMs) can improve the properties of concrete, reduce pressure on natural resources and CO2 emissions. However, certain SCMs are unable to meet the growing demand of construction sector. The presented study investigates the role of bentonite clay (BC) and its synergistic effect with silica fume (SF) as partial replacement of cement on strength, durability, and microstructure of concrete. Five different mixtures were prepared containing 0%, 7.5%, 15% and 22.5% (by weight) of BC as a replacement of cement whereas SF was kept constant at 10% replacement level. The experimental results showed that the addition of SF had a positive impact on compressive strength with increase in curing time i.e., a maximum value of 43.09 MPa was achieved. The bentonite based concrete requires excess curing to achieve high strength. The ultrasonic pulse velocity of all tested specimen lies in the range of "good" quality concrete i.e., > 3.5 km/s. Durability testing depicted that the BC and SF significantly improved the pore structures and resistance against sulfate attack and chloride ingress. The proposed empirical formulations (using machine learning technique i.e., gene expression programming) were found accurate (correlation>0.9) and can be utilized for prediction of properties of concrete. The environmental impact analysis revealed that utilization of BC and SF can reduce the carbon emission by approximately 23% compared to control mix. It is recommended to conduct leachate analysis for each mixture prior to use. The proposed concrete mixtures were deemed technically, environmentally, and economically viable for application in construction sector.

关键词： Secondary cementitious materials (SCMs) Synergistic effect Sustainable concrete Durability Environmental impact Machine learning gene expression programming

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gene expression Models for the Prediction of Longitudinal Dispersion Coefficients in Transitional and Turbulent Pipe Flow

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2014年第1期5卷

作者： Sattar, Ahmed M. A. Cairo Univ Fac Engn Dept Irrigat & Hydraul Giza 12316 Egypt

Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of evolutionary gene expression programming (GEP) to develop new empirical formulas for the prediction of longitudinal dispersion coefficients in pipe flow using 220 experimental case studies of the dispersion coefficient with a R range of 2,000-500,000 spanning transitional and turbulent pipe flow. gene expression programming is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient, and the pipe diameter. Four GEP models are developed, and the weight and importance of each control variable is presented. The prediction uncertainties of all of the developed GEP models are quantified and compared with those of existing models. Finally, a parametric analysis is performed for further verification of the developed GEP models. The results indicate that the proposed relations are simple and can effectively evaluate the longitudinal dispersion coefficients in pipe flow. (C) 2013 American Society of Civil Engineers.

关键词： Longitudinal dispersion coefficient Axial mixing in pipes Pipe flow Pipe network Water quality Evolutionary algorithms gene expression programming Uncertainty analysis Parametric analysis

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EV charging load forecasting model mining algorithm based on hybrid intelligence

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COMPUTERS & ELECTRICAL ENGINEERING 2023年 112卷

作者： Deng, Song Wang, Junjie Tao, Li Zhang, Su Sun, Hongwei Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

Precise EV charging load forecasting plays a critical role in optimizing resource allocation and facilitating economic operation and energy management of EV charging stations, as well as supporting the economic dispatch of the power grid for efficient and effective utilization of resources. Existing EV charging load forecasting models have highly restrictive load data requirements and thus have practical limitations in two fold: first, these models are a black box and cannot provide a quantitative reference for later analysis affecting EV charging load forecasting;and second, they fail in considering the impact of noisy data caused by uncontrollable factors (data collection failures, human errors, and network attacks, etc.) on the accuracy of EV charging load forecasting. To address the above issues, we propose a novel EV charging load forecasting model mining based on gene expression programming (CFMM-GEP) by fusing noisy load processing. This will tackle three-fold ideas: (1) the charging load dataset with abnormal data is reconstructed based on the Auto-Encoder, and the support vector machine -based abnormal detection algorithm is proposed;(2) a quantitative model for EV charging load forecasting based on gene expression programming is constructed. Extensive experiments are carried out on four open-source charging load datasets. Experimental results indicate that the superiority of our proposed CFMM-GEP model over 6 state-of-the-art models in terms of MAP E, RMSE, MAE, and R2.

关键词： Electric vehicles Charging load forecasting gene expression programming Function mining

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A dynamic scheduling method for self-organized AGVs in production logistics systems

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Procedia CIRP 2021年 104卷 381-386页

作者： Lixiang Zhang Yan Yan Yaoguang Hu Weibo Ren School of Mechanical Engineering Beijing Institute of Technology Beijing China

Automated guided vehicles (AGV) with different carrying capacities are required for complex material handling in smart factories, which causes resource waste. To minimize the delay and reduce the cost of logistics systems, this paper proposes a dynamic scheduling method for self-organized AGVs (SAGV) in production logistics systems, where multiple identical SAGVs can communicate and freely combine with others as one vehicle to perform one task. Using an improved gene expression programming to learning dynamic dispatching rules, experimental results show that dispatching rules learned are efficient and the cost of logistic systems by using SAGVs is significantly reduced.

关键词： dynamic scheduling automated guided vehicles gene expression programming production logistics system

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An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

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JOURNAL OF BUILDING ENGINEERING 2024年 86卷

作者： Al-Hamd, Rwayda Kh S. Albostami, Asad S. Alzabeebee, Saif Al -Bander, Baidaa Abertay Univ Sch Appl Sci Dundee Scotland Univ Petra Fac Engn Civil Engn Dept Amman Jordan Oryx Universal Coll Partnership Liverpool John Moo Sch Engn & Construct Doha Qatar Univ Al Qadisiyah Coll Engn Dept Rd & Transport Engn Al Diwaniyah Al Qadisiyah Iraq Keele Univ Sch Comp Newcastle Upon Tyne ST5 5BG Keele England

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars' bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly gene expression programming (GEP) and the Multi-Objective genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR). The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete.

关键词： Multi -objective genetic algorithm evolutionary polynomial regression gene expression programming Soft computing Fiber reinforcement polymer Bond strength

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Artificial intelligence assisted MOPSO strategy for discerning the exergy efficiency potential of a methanol induced RCCI endeavour through GA coupled multi-attribute decision making approach

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ENERGY CONVERSION AND MANAGEMENT 2021年 248卷 114727-114727页

作者： Kakati, Dipankar Biswas, Srijit Banerjee, Rahul NIT Agartala Dept Mech Engn Agartala 799046 India

The present study undertakes a comprehensive effort to explore the exergy efficiency-emissions-stability-combustion quality characteristics of premixed methanol with diesel reactivity controlled operation. The com-bustion phasing of the dual fuel operation primarily depends on the methanol participation rate, wherein the peak in-cylinder pressure and heat release rate decreases with increased methanol injection duration. Though simultaneous reductions of NOx and soot were observed in this study, the stability of the operations deteriorates along with unburned hydrocarbon (UHC) and carbon monoxide (CO) with increasing methanol participations resulting in a trade-off situation. Besides, the severe instability of the operation at 50% load causes misfire due to excessive dilution of the charge at higher methanol participation. The study further explores the potential of gene expression programming assisted meta-model coupled Multi-objective Particle Swarm optimization (MOPSO) algorithm based multi-objective optimization endeavour to explore the optimal operational design space considering the multiple responses of exergy efficiency, NOx, PM, UHC, CO, Coefficient of Variance of indicated mean effective pressure (COVIMEP), lowest normalized value (LNV) of indicated mean effective pres-sure. In this present case of study, the optimization endeavor has yielded 350 numbers of Pareto solutions, while only 26 numbers of Pareto solutions were observed in the experimental counterpart. Moreover, the experimental domain of the present study has produced only single set of experiment which can satisfy the respective emission limits of NHC and PM altogether, whereas 13 sets were evident in the optimization study to maintain the NHC and PM footprint under the emission constraints simultaneously. The overall analysis of the Pareto solutions evolved in the optimization study has revealed that to attain the minimum NHC and PM footprints, the penalty of exergy efficiency and CO emissions

关键词： Port premixed methanol-diesel RCCI combustion Exergy efficiency Performance-emissions-stability trade-off gene expression programming Multiobjective optimization MOPSO

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A Comparative Study of MARS, GEP, and GMDH Methods for Modeling Soil Thermal Conductivity

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INTERNATIONAL JOURNAL OF THERMOPHYSICS 2023年第8期44卷 115-115页

作者： Al-Hawary, Sulieman Ibraheem Shelash Muda, Iskandar Sayed, Biju Theruvil Fenjan, Mohammed N. N. Kareem, A. K. Al Al Bayt Univ Dept Business Adm POB 130040 Mafraq 25113 Jordan Univ Sumatera Utara Dept Doctoral Program Medan Indonesia Dhofar Univ Dept Comp Sci Salalah Oman Al Ayen Univ Coll Hlth & Med Technol Thi Qar Iraq Al Mustaqbal Univ Coll Biomed Engn Dept Babylon Iraq

The present study used three well-known white-box data-driven models, including multivariate adaptive regression splines (MARS), gene expression programming (GEP), and group method of data handling (GMDH), for generating explicit formulas for the prediction of thermal conductivity of the soil (lambda). Therefore, 40 soil samples and three input variables, such as moisture content (omega), porosity (n), and the natural density of soil (rho), were used to predict lambda. The performance of the proposed formulas was assessed via statistical indicators such as the determination of coefficient (R-2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Statistical criteria have shown that all proposed models provided almost identical results. However, the MARS model was marginally more accurate than the GEP and GMDH models. In addition, the error measures of MARS with RMSE = 0.021, MAE = 0.018, and MAPE = 1.191% were slightly more accurate than GA-ANN (RMSE = 0.030, MAE = 0.025, and MAPE = 1.750%) that reported in the previous study for estimation of lambda. However, the prominent feature of the suggested white-box data-driven models compared to black-box models such as ANN is to provide explicit equations for estimating lambda.

关键词： Data-driven models gene expression programming Group method of data handling Multivariate adaptive regression splines Soil thermal conductivity

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Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin

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MATERIALS 2022年第15期15卷 5435-5435页

作者： Shah, Hammad Ahmed Yuan, Qiang Akmal, Usman Shah, Sajjad Ahmad Salmi, Abdelatif Awad, Youssef Ahmed Shah, Liaqat Ali Iftikhar, Yusra Javed, Muhammad Haris Khan, Muhammad Imtiaz Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Natl Engn Res Ctr High Speed Railway Construct Te Changsha 410075 Peoples R China Univ Engn & Technol Dept Civil Engn Lahore 54890 Pakistan Cent South Univ Sch Geosci & Info Phys Changsha 410075 Peoples R China Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Civil Engn Al Kharj 16273 Saudi Arabia Future Univ Egypt Fac Engn & Technol Struct Engn Dept New Cairo 11835 Egypt Univ Engn & Technol Peshawar Dept Civil Engn Bannu 28100 Pakistan CECOS Univ IT & Emerging Sci Dept Civil Engn Peshawar 25000 Pakistan

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f(c)'), splitting tensile strength (f(st)), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f(c)', f(st), and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f(c)' and f(st).

关键词： gene expression programming artificial neural network M5P random forest metakaolin compressive strength

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