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Air Temperature Prediction Models for Pavements Based on the gene expression programming Approach

JOURNAL OF TRANSPORTATION ENGINEERING PART B-PAVEMENTS 2025年第1期151卷

作者： Padala, Suresh Kumar Swamy, Aravind Krishna Bhattacharjee, Bishwajit Indian Inst Technol Bhubaneswar Sch Infrastruct Khordha 752050 Orissa India Indian Inst Technol Delhi Dept Civil Engn Principal Project Scientist New Delhi 110016 India Indian Inst Technol Delhi Dept Civil Engn New Delhi 110016 India

As per the performance grading scheme, the selection of asphalt binder for a particular location requires information on seven-day maximum and one-day minimum pavement temperatures. Pavement surface temperatures are usually related to the surrounding air temperature. This study presents a methodology for developing air temperature predictive models using high resolution long-term weather data of India. gene expression programming (GEP), an evolutionary computing algorithm, was used to evaluate the expressions governing the air temperature as a function of latitude, longitude, elevation, relative humidity, wind speed, solar radiation, and rainfall intensity. A new methodology to evaluate the optimum tree depth for achieving reasonably high accuracy but at reasonably smaller tree depth was also proposed. Statistical analysis involving comparing the goodness of fit and distribution of the prediction error was conducted to understand the prediction capability of the proposed models. The statistical analysis proved the reasonably high predictive power of the gene expressions corresponding to the optimum tree depth. The proposed seven-day maximum and one-day minimum air temperature predictive models have a very simple structure that can be used by field engineers for hand calculation with little effort.

关键词： gene expression programming Air temperature Asphalt pavement temperature Optimum tree depth

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gene expression programming analysis of implicit Colebrook-White equation in turbulent flow friction factor calculation

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2012年 92-93卷 48-55页

作者： Samadianfard, Saeed Univ Tabriz Water Engn Dept Tabriz Iran

Estimating head losses due to friction in closed pipes is an important task in the solution of many practical problems in the different branches of the engineering profession. The hydraulic design and analysis of water distribution systems are two prime examples. Many consider the Darcy-Weisbach equation to be the most fundamentally sound method for evaluating head losses due to friction in closed pipe conduits. The implicit Colebrook-White equation has been widely used to estimate the friction factor for turbulent fluid-flow in Darcy-Weisbach equation. A fast, accurate, and robust resolution of the Colebrook-White equation is, in particular, necessary for scientific intensive computations. For instance, numerical simulations of pipe flows require the computation of the friction coefficient at each grid point and for each time step. For long-term simulations of long pipes, the Colebrook-White equation must therefore be solved a huge number of times and hence this is the main reason for attempting to develop an accurate explicit relationship that is a reasonable approximation for the Colebrook-White equation. This paper examines the potential of genetic programming (GP) based technique in estimating flow friction factor in comparison with the most currently available explicit alternatives to the Colebrook-White equation. The performance of the gene expression programming (GEP), a variant of GP, was compared with most available approximations using some statistic parameters for error estimation. The comparison test results reveal that by using GEP, the friction factor can be identified precisely. (c) 2012 Elsevier B.V. All rights reserved.

关键词： approximations Colebrook-White friction factor Darcy-Weisbach equation gene expression programming turbulent flow

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gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds

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FLUID PHASE EQUILIBRIA 2012年 329卷 71-77页

作者： Gharagheizi, Farhad Ilani-Kashkouli, Poorandokht Farahani, Nasrin Mohammadi, Amir H. Islamic Azad Univ Dept Chem Engn Buinzahra Branch Buinzahra Iran Islamic Azad Univ Dept Chem Buinzahra Branch Buinzahra Iran MINES ParisTech CEP TEP Ctr Energet & Proc F-77305 Fontainebleau France Univ KwaZulu Natal Thermodynam Res Unit Sch Chem Engn ZA-4041 Durban South Africa

The accuracy and predictability of correlations and models to determine the flammability characteristics of chemical compounds are of drastic significance in various chemical industries. In the present study, the main focus is on introducing and applying the gene expression programming (GEP) mathematical strategy to develop a comprehensive empirical method for this purpose. This work deals with presenting an empirical correlation to predict the flash point temperature of 1471 (non-electrolyte) organic compounds from 77 different chemical families. The parameters of the correlation include the molecular weight, critical temperature, critical pressure, acentric factor, and normal boiling point of the compounds. The obtained statistical parameters including root mean square of error of the results from DIPPR 801 data (8.8, 8.9, 8.9 K for training, optimization and prediction sets, respectively) demonstrate improved accuracy of the results of the presented correlation with respect to previously-proposed methods available in open literature. (C) 2012 Elsevier B.V. All rights reserved.

关键词： gene expression programming Flammability characteristics Flash point genetic programming genetic algorithm

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gene expression programming based due date assignment in a simulated job shop

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第10期36卷 12143-12150页

作者： Baykasoglu, Adil Gocken, Mustafa Univ Gaziantep Dept Ind Engn TR-27310 Gaziantep Turkey

In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Due date assignment Flowtime estimation gene expression programming Simulation

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gene expression programming as a data classification tool. A review

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期36卷 91-100页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Fac Math Phys & Informat Inst Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Gdynia Poland

The paper reviews classification algorithms based on gene expression programming (GEP) used for mining the real-life datasets. Our aim is to show, chronologically, most important developments as well as the current state-of-the-art in the area of GEP-based classifiers, with a view to attract further real life applications. We begin with reviewing approaches to building basic, stand alone, GEP classifiers and eventually, combining them into the classifier ensemble. In the following part of the paper we describe and illustrate with example several hybrid solutions where GEP is integrated with other methods. Next, we review specialized and dedicated methods including multiple criteria and incremental GEP-based classification tools. Final part of the paper reviews specialized GEP-based classifiers used to mine the real-life datasets.

关键词： gene expression programming classification classifier ensemble

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gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia

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APPLIED WATER SCIENCE 2018年第6期8卷 1页

作者： Azamathulla, H. Md. Rathnayake, Upaka Shatnawi, Ahmad Univ Tabuk Dept Civil Engn Tabuk 71491 Saudi Arabia Univ Tabuk SNCS Res Ctr Tabuk 71491 Saudi Arabia Sri Lanka Inst Informat Technol Dept Civil Engn Malabe 10115 Sri Lanka Univ West Indies St Augustine Campus St Augustine Trinidad Tobago

Climate change is not a myth. There is enough evidence to showcase the impact of climate change. Town planners and authorities are looking for potential models to predict the climatic factors in advance. Being an agricultural area in Saudi Arabia, Tabuk region gets greater interest in developing such a model to predict the atmospheric ***, this paper presents two different studies based on artificial neural networks (ANNs) and gene expression programming (GEP) to predict the atmospheric temperature in Tabuk. Atmospheric pressure, rainfall, relative humidity and wind speed are used as the input variables in the developed models. Multilayer perceptron neural network model (ANN model), which is high in precession in producing results, is selected for this study. The GEP model that is based on evolutionary algorithms also produces highly accurate results in nonlinear models. However, the results show that the GEP model outperforms the ANN model in predicting atmospheric temperature in Tabuk region. The developed GEP-based model can be used by the town and country planers and agricultural personals. [GRAPHICS] .

关键词： Artificial neural network Atmospheric temperature Climate change gene expression programming Tabuk

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gene expression programming for total bed material load estimation-a case study

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SCIENCE OF THE TOTAL ENVIRONMENT 2010年第21期408卷 5078-5085页

作者： Zakaria, Nor Azazi Azamathulla, H. Md. Chang, Chun Kiat Ghani, Aminuddin Ab. Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Nibong Tebal 14300 Pulau Pinang Malaysia

This paper presents gene-expression programming (GEP), which is an extension to the genetic programming (GP) approach to predict the total bed material load for three Malaysian rivers. The GEP is employed without any restriction to an extensive database compiled from measurements in the Muda, Langat, and Kurau rivers. The GEP approach demonstrated a superior performance compared to other traditional sediment load methods. The coefficient of determination, R-2 (= 0.97) and the mean square error, MSE (= 0.057) of the GEP method are higher than those of the traditional method. The performance of the GEP method demonstrates its predictive capability and the possibility of the generalization of the model to nonlinear problems for river engineering applications. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Alluvial channels Sediment transport Total bed material load gene expression programming River engineering

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gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia

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NATURAL HAZARDS 2022年第2期113卷 1349-1365页

作者： Hosseini, Maryamsadat Lim, Samsung Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods.

关键词： gene expression programming Bushfire Susceptibility map Machine learning

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gene expression programming-based viscosity-mixing rule for asphalt blends

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PETROLEUM SCIENCE AND TECHNOLOGY 2017年第14期35卷 1508-1514页

作者： Eleyedath, Abhary Swamy, Aravind Krishna Indian Inst Technol Delhi New Delhi India Indian Inst Technol Delhi Civil Engn Hauz Khas New Delhi 110016 India

Mixing rule plays a significant role in the prediction of resultant viscosity of asphalt/oil blends. This work presents a novel gene expression programming (GEP)-based approach to obtain a viscosity-mixing rule. To develop these expressions, two distinct binary blends comprising of varying proportion of unmodified binder and polymer modified binder were prepared and tested for their resultant viscosity at different temperatures. The obtained data were used to (i) develop the GEP-based viscosity-mixing rule and (ii) calculate resultant viscosity with viscosity-mixing rules reported in the literature (like the Arrhenius model). Statistical analysis indicated that the accuracy of GEP-based mixing rule is superior over other viscosity-mixing rules reported in the literature.

关键词： artificial intelligence asphalt binary mixtures bitumen diluent gene expression programming statistical analysis viscosity viscosity-mixing rule

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gene expression programming based meta-modelling approach to production line design

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2008年第6期21卷 657-665页

作者： Baykasoglu, A. Gaziantep Univ Dept Ind Engn TR-27310 Gaziantep Turkey

Fierce competition in today's economy forces companies to fully optimize their processes in order to supply customers with high-quality products on time with lowest possible cost. Designing optimal production lines is a major step ahead in satisfying customer needs. Owing to the stochastic and highly nonlinear nature of the production lines, their optimal design is not easy and requires usage of advanced tools and techniques. In the present paper one of the new generation soft computing technique that is known as gene expression programming (GEP) is used to develop a meta-model from extensive simulation experiments for the multiple objective design of a production line. The developed meta-model is used to optimize production line design with multiple objective tabu search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to model and solve production line design problems.

关键词： production line design soft computing gene expression programming simulation meta-modelling

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