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Analytical and genetic programming model of compressive strength of eco concretes by NDT according to curing temperature

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CONSTRUCTION AND BUILDING MATERIALS 2017年 144卷 195-206页

作者： Velay-Lizancos, Mirian Luis Perez-Ordonez, Juan Martinez-Lage, Isabel Vazquez-Burgo, Pablo Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Univ A Coruna Ctr Innovac Tecnol Edificac & Ingn Civil CITEEC Grp Construct ETS Ingn Caminos Canales & Puertos Campus Elvina S-N La Coruna 15071 Spain

The use of non-destructive testing for estimating the compressive strength of concrete has great advantages both short term and long term. In the case of eco-concrete with recycled materials, it is of particular interest, since its use in many regulations is conditional on further studies. In this research we analyze the applicability of the most common models for estimating compressive strength by combining nonde-structive testing. Specifically it was applied to 11 concrete with cement CEM-I, 3 self-compacting concrete and 8 vibrated concrete. There are two reference concretes (one of each) and the rest of concretes either have changed water/cement ratio or they contain different percentages of recycled materials (recycled aggregate fine and coarse together, or biomass ashes). Destructive tests have been made (compressive strength) and non-destructive (ultrasonic pulse velocity and compressive strength) in all concretes, at different ages and different curing temperatures, obtaining a total of 181 data sets. New estimation models were proposed for compressive strength with factors such as the curing temperature, the temperature history, the density of the concrete and the quantity of additive. These models substantially improve the results obtained with the usual methods. Finally, using genetic programming, it has managed to obtain an equation that allows, safely, estimating compressive strength with the information of non-destructive testing. The equation obtained improves current predictions with the peculiarity that minimizes uncertain results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ultrasonic pulse velocity Concrete Estimation of compressive strength genetic programming model NDT Recycled aggregate Biomass ash Compressive strength Eco-concrete Maturity

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Functionalized copper oxide-zinc oxide nanocomposite: synthesis and genetic programming model of dye adsorption

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DESALINATION AND WATER TREATMENT 2016年第40期57卷 18755-18769页

作者： Mahmoodi, Niyaz Mohammad Chamani, Hooman Kariminia, Hamid-Reza Inst Color Sci & Technol Dept Environm Res Tehran Iran Sharif Univ Technol Dept Chem & Petr Engn Tehran Iran

The functionalized copper oxide-zinc oxide nanocomposite (FCZN) was synthesized and characterized using Fourier transform infrared, scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and BET. Dye removal from aqueous solution was done in a batch system using FCZN as an adsorbent. The effects of adsorbent dosage, initial dye concentration, pH, temperature, and additive salts on dye removal were investigated. Isotherms, kinetics, and thermodynamics of dye adsorption were studied. Equilibrium and kinetic data were fitted by Langmuir isotherm and pseudo-second-order kinetic, respectively. The thermodynamic data showed that dye adsorption was spontaneous, endothermic, and physical reaction. In addition, genetic programming (GP) was applied in order to predict dye removal using an explicit formula. The results of proposed GP models were in close agreement with the experimental data.

关键词： Nanocomposite synthesis Dye removal genetic programming model Isotherm Kinetic Thermodynamic

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Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing models

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WATER RESOURCES MANAGEMENT 2023年第9期37卷 3563-3584页

作者： Yarahmadi, Mohammad Bahrami Parsaie, Abbas Shafai-Bejestan, Mahmood Heydari, Mostafa Badzanchin, Marzieh Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Ahvaz Iran Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Civil Engn Water & Hydraul Struct Ahvaz Iran

Flow conditions (flow discharge, flow depth, and flow velocity) in natural streams are mainly determined via the flow resistance formula such as Manning's equation. Evaluating the accurate Manning's roughness coefficient (n), especially in rivers with bed form during floods, to obtain more reliable results has always been of interest to scholars. The interaction between the flow and bed form is very complex since the flow conditions control bed forms, and vice versa. The main goal of the present study is to predict n in rivers with bed forms, using soft computing models, including multilayer perceptron artificial neural network (MLPNN), group method of data handling (GMDH), support vector machine (SVM) model, and genetic programming model (GP). To this end, the energy grade line (Sf), flow Froude number (Fr), the relative submergence (y/d(50);y= flow depth and d(50) = bed sediment size), and the bed form dimensionless parameters ( ?/d(50), ?/A, and ?/?;? = bed form height and ?= bed form length) were used as the input variables, and n was used as the output variable. The results showed that all the test models have acceptable accuracy, while the SVM model showed the highest level of accuracy with the coefficient of determination R-2 = 0.99 in the verification stage. The sensitivity analysis of SVM and MLPNN models and the structural analysis of GMDH and GP models indicated that the most important parameters affecting n are Fr, S-f,S- and ?/?.

关键词： Bed form Flow resistance genetic programming model Group method of data handling Support vector machine

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Multi-objective design optimization of battery thermal management system for electric vehicles

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APPLIED THERMAL ENGINEERING 2021年 196卷 117235-117235页

作者： Su, Shaosen Li, Wei Li, Yongsheng Garg, Akhil Gao, Liang Zhou, Quan Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China Chongqing Univ Coll Mech Engn Chongqing Peoples R China Univ Birmingham Dept Mech Engn Birmingham B15 2TT W Midlands England

Lithium batteries are commonly used as the primary power storage unit for electric vehicles, and their performance is sensitive to temperature. Thus, the battery thermal management system is crucially needed to allow the EVs to work safely and efficiently. This paper mainly focuses on the performance analysis and design optimization of the battery thermal management system with a U-shaped cooling channel. A Computational fluid dynamics model of a battery thermal management system is built to study the battery temperature distribution and pressure distribution. Through the establishment of the genetic programming model, sensitivity analysis and parameter interaction analysis are carried out to analyze the influence of cooling plate thickness, cooling plate wall thickness, inlet coolant temperature and flow velocity on the comprehensive performance of the battery thermal management system. A new surrogate-assisted multi-objective optimization scheme is proposed by introducing an integrated AI system that includes a surrogate battery model built with genetic programming (GP) and a design optimizer driven by the second-generation non-dominated sorting genetic algorithm (NSGA-II). Results show that the inlet coolant temperature has the most significant influence on the rise of battery temperature (59.87%) but has no influence on the pressure drop. The structural parameters of the cooling plate and the velocity of the inlet coolant have apparent effects on the uniformity of the battery temperature distribution and the pressure drop. The battery thermal management system achieves an ideal comprehensive performance when the thickness of the cooling plate is 4.50 mm, the thickness of the cooling plate wall is 1.49 mm, the inlet coolant temperature is 298.15 K, and the inlet coolant velocity is 0.29 m/s. Under such optimized parameter settings, the max temperature rise of the battery reduces from 7.72 K to 7.69 K, the standard deviation of the temperature distribution 2.54 K

关键词： Serpentine channel U-shaped channel genetic programming model Liquid cooling

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Effect analysis on performance enhancement of a novel air cooling battery thermal management system with spoilers

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APPLIED THERMAL ENGINEERING 2021年 192卷 116932-116932页

作者： Wang, Ningbo Li, Congbo Li, Wei Huang, Mingli Qi, Dongfeng Chongqing Univ Coll Mech Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

To solve a series of thermal runaway problems caused by temperature and the cost problem caused by the excessive volume of the battery thermal management system (BTMS), this paper presents a novel air cooling BTMS which reduces the temperature and volume. In this study, we install the spoilers in the battery gap spacing, which can effectively improve the heat dissipation performance of the battery. Firstly, this paper discusses the influence of the shape, number and length of the spoilers on the maximum temperature (Max(T)) and temperature uniformity of the battery module. After computational fluid dynamics (CFD) simulation, this paper takes a BTMS with 16 long straight spoilers as plan 1. Compared with the initial plan without spoilers, the Max(T) of plan 1 is reduced by 3.52 K. Secondly, Latin hypercube sampling (LHS) is used to sample and then establish the genetic programming (GP) model for the Max(T) and the volume of plan 1. Finally, this paper combines CFD simulation with the multi-objective genetic algorithm (MOGA) to drive the optimization process. The optimization results show that the Max(T) of the battery module is 307.58 K, and the volume of BTMS is 12644460 mm(3). Compared with plan 1, the Max(T) is reduced by 2.24 K, and the volume is reduced by 4.87%. This result has guiding significance for improving the heat dissipation of Z-shaped air cooling BTMS and saving the cost in the industry.

关键词： Battery thermal management system Air cooling Spoiler genetic programming model

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Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears

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FUNCTIONAL ECOLOGY 2021年第2期35卷 398-410页

作者： Rogers, Savannah A. Robbins, Charles T. Mathewson, Paul D. Carnahan, Anthony M. van Manen, Frank T. Haroldson, Mark A. Porter, Warren P. Rogers, Taylor R. Soule, Terence Long, Ryan A. Univ Idaho Bioinformat & Computat Biol Program Moscow ID 83843 USA Washington State Univ Sch Environm Pullman WA 99164 USA Washington State Univ Sch Biol Sci Pullman WA 99164 USA Univ Wisconsin Madison Dept Integrat Biol Madison WI USA Washington State Univ Sch Biol Sci Pullman WA 99164 USA US Geol Survey Interagency Grizzly Bear Study Team Bozeman MT USA Univ Idaho Dept Comp Sci Moscow ID 83843 USA Univ Idaho Dept Fish & Wildlife Sci Moscow ID 83843 USA

Heat dissipation limit theory posits that energy available for growth and reproduction in endotherms is limited by their ability to dissipate heat. In mammals, endogenous heat production increases markedly during gestation and lactation, and thus female mammals may be subject to greater thermal constraints on energy expenditure than males. Such constraints likely have important implications for behaviour and population performance in a warming climate. We used a mechanistic simulation model based on the first principles of heat and mass transfer to study thermal constraints on activity (both timing and intensity) of captive female grizzly bears Ursus arctos in current and future climate scenarios. We then quantified the relative importance of regulatory behaviours for maintaining heat balance using GPS telemetry locations of lactating versus non-lactating female bears from Yellowstone National Park, and assessed the degree to which costs of thermoregulation constrained the distribution of sampled bears in space and time. Lactating female bears benefitted considerably more from behavioural cooling mechanisms (e.g. partial submersion in cool water or bedding on cool substrate) than non-lactating females in our simulations;the availability of water for thermoregulation increased the number of hours during which lactating females could be active by up to 60% under current climatic conditions and by up to 43% in the future climate scenario. Moreover, even in the future climate scenario, lactating bears were able to achieve heat balance 24 hr/day by thermoregulating behaviourally when water was available to facilitate cooling. The most important predictor of female grizzly bear distribution in Yellowstone, regardless of reproductive status, was elevation. However, variables associated with the thermal environment were relatively more important for predicting the distribution of lactating than non-lactating female bears. Our results suggest that the costs of heat dissipati

关键词： behaviour genetic programming model Greater Yellowstone Ecosystem heat dissipation limit theory lactation spatiotemporal distribution thermoregulation Ursus arctos

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Prediction models in the design of neural network based ECG classifiers: Aneural network and genetic programming approach

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BMC Medical Informatics and Decision Making 2002年第1期2卷 1-6页

作者： Nugent, Chris D. Lopez, Jesus A. Smith, Ann E. Black, Norman D. Medical Informatics Research Group Faculty of Informatics University of Ulster Jordanstown BT37 0QB United Kingdom

Background: Classification of the electrocardiogram using Neural Networks has become a widely used method in recent years. The efficiency of these classifiers depends upon a number of factors including network training. Unfortunately, there is a shortage of evidence available to enable specific design choices to be made and as a consequence, many designs are made on the basis of trial and error. In this study we develop prediction models to indicate the point at which training should stop for Neural Network based Electrocardiogram classifiers in order to ensure maximum generalisation. Methods: Two prediction models have been presented;one based on Neural Networks and the other on genetic programming. The inputs to the models were 5 variable training parameters and the output indicated the point at which training should stop. Training and testing of the models was based on the results from 44 previously developed bi-group Neural Network classifiers, discriminating between Anterior Myocardial Infarction and normal patients. Results: Our results show that both approaches provide close fits to the training data;p = 0.627 and p = 0.304 for the Neural Network and genetic programming methods respectively. For unseen data, the Neural Network exhibited no significant differences between actual and predicted outputs (p = 0.306) while the genetic programming method showed a marginally significant difference (p = 0.047). Conclusions: The approaches provide reverse engineering solutions to the development of Neural Network based Electrocardiogram classifiers. That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

关键词： genetic programming Feature Selection Method Unseen Data Anterior Myocardial Infarction genetic programming model

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