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Application of artificial intelligence methods to model the effect of grass curing level on spread rate of fires

ENVIRONMENTAL MODELLING & SOFTWARE 2024年 173卷

作者： Khanmohammadi, Sadegh Cruz, Miguel G. Golafshani, Emadaldin Mohammadi Bai, Yu Arashpour, Mehrdad Monash Univ Dept Civil Engn Melbourne Australia CSIRO GPO Box 1700 Canberra ACT 2601 Australia

Artificial intelligence (AI) enables new approaches to fire behaviour models of operational relevance, including prescribed burns. This is particularly important in modelling of processes that are poorly understood, such as live fuel's effect on fire propagation. The objective of this study was to apply AI algorithms to quantify the effect of the proportion of dead fuels in a senescing grassland, the curing level, on reducing the rate of fire spread relative to the fully cured condition. We applied three different machine learning (ML) models, regression trees, support vector regression (SVR) and gene expression programming (GEP), two ensemble ML methods, Random Forest and GEP Forest, and non-linear regression analysis to an experimental fire dataset. Results show SVR and GEP as the best ML methods to model the curing level impact on fire spread. No differences in model fit were observed between the best ML methods and non-linear regression analysis.

关键词： Bushfire Feature importance gene expression programming Grasslands Machine learning Remote region

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Experimental based multi-objective optimisation for structured packed bed liquid desiccant dehumidification systems

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JOURNAL OF BUILDING ENGINEERING 2020年 32卷

作者： Bhowmik, Mrinal Muthukumar, P. Anandalakshmi, R. Indian Inst Technol Guwahati Ctr Energy Gauhati 781039 India Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 India Indian Inst Technol Guwahati Dept Chem Engn Adv Energy & Mat Lab Gauhati 781039 India

In last few decades, a substantial advancement has been observed in dehumidifier modules for their energy-conservative and environment-friendly nature;however, still uncertainty in decision-making is of concern in selection of optimum input parameters to attain an effective performance. In this perspective, an experimental study is performed in an adiabatic packed bed, counter-flow liquid desiccant dehumidifier fuelled with the blend of lithium bromide and calcium chloride solution at various operating conditions with different control settings. It is observed that air mass flux rate (F-a), solution concentration (zeta) and air specific humidity (omega(a)) have significant impact on condensation rate (CR), whereas, air mass flux rate (F-a) and solution temperature (T-s) are the most effective parameters for moisture effectiveness and latent heat factor, respectively. The experimentally obtained results for multiple response performance characteristics are represented based on inlet process parameters using gene expression programming (GEP). The proficiency of developed GEP meta-models is provided by statistical parameters, Taylor diagram and Theil uncertainty. Subsequently, GEP meta-model based fuzzy logic is developed for optimising dehumidifier inlet process parameters in terms of multi-responsive performance characteristics using genetic Algorithm. The optimisation results showed that dehumidifier performance characteristics have a tendency to become optimal at F-a = 0.766 kg/m(2)-s, T-a = 30.745 degrees C, omega(a) = 0.023 kg(WV)/kg(da), F-s = 1.812 kg/m(2)-s, T-s = 24.01 degrees C, and zeta = 48.1%. Finally, experiments were conducted at optimum conditions and observed proximity between the predicted and experimental outcomes, which further validates the current optimisation approach.

关键词： Dehumidifier gene expression programming Condensation rate Moisture effectiveness Latent heat factor

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Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling

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CHEMOSPHERE 2023年 313卷 137336页

作者： Yaqub, Muhammad Nguyen, Mai Ngoc Lee, Wontae Kumoh Natl Inst Technol Dept Environm Engn 61 Daehak Ro Gumi 39177 South Korea

Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (133Cs+) and natural organic matter (NOM) from an aqueous solution. The Cs+ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs+ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs + adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs+ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs+ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R2 values of 0.9348 and 0.889, respectively.

关键词： Adsorption Cesium gene expression programming Heated aluminum oxide particles Natural organic matter Prussian blue

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Developing novel hybrid models for estimation of daily soil temperature at various depths

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SOIL & TILLAGE RESEARCH 2020年 197卷

作者： Mehdizadeh, Saeid Fathian, Farshad Safari, Mir Jafar Sadegh Khosravi, Ali Urmia Univ Dept Water Engn Orumiyeh Iran Vali E Asr Univ Rafsanjan Fac Agr Dept Water Sci & Engn POB 77188-97111 Rafsanjan Iran Yasar Univ Dept Civil Engn Izmir Turkey Aalto Univ Sch Engn Dept Mech Engn Helsinki Finland

Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time series-based model, namely fractionally autoregressive integrated moving average (FARIMA), as well as two machine learning-based models consisting of feed-forward back propagation neural networks (FFBPNN) and gene expression programming (GEP) for daily ST estimation. In doing so, the daily ST data of three stations at four depths (5, 10, 50, and 100 cm) in Iran were used for the time period from 1998 to 2017. Studied stations were selected from different climates including arid (Isfahan station), semi-arid (Urmia station), and very humid (Rasht station) to evaluate the performance of models and generalize the outcomes in different climate classes. The performances of the developed models are evaluated via three statistical metrics including the root mean square error (RMSE), mean absolute error (MAE), and relative RMSE (RRMSE). Results obtained demonstrated that the machine learning-based FFBPNN and GEP models performed better than the time series-based FARIMA approach at all depths. As a result, negligible differences were observed between the accuracies of FFBPNN and GEP. In addition, this study developed novel hybrid models through combining the FFBPNN and GEP techniques with the FARIMA to enhance the accuracy of traditional FARIMA, FFBPNN, and GEP. The developed hybrid models named GEP-FARIMA and FFBPNN-FARIMA were found to achieve better estimates of daily ST data at different depths in comparison with the classical models. The daily ST estimates with the highest accuracy were observed at a depth of 50 cm via the GEP-FARIMA at Isfahan station (RMSE = 0.05 degrees C, MAE = 0.03 degrees C, RRMSE = 0.25% for the testing phase), the GEP-FARIMA at Urmia station (RMSE = 0.04 degrees C, MAE = 0.03 degrees C, RRMSE = 0.26% for the testing phase), and the F

关键词： Estimation Daily soil temperature Fractionally autoregressive integrated moving average Feed-forward back propagation neural networks gene expression programming

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Mathematical model and augmented simulated annealing algorithm for mixed-model assembly job shop scheduling problem with batch transfer

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KNOWLEDGE-BASED SYSTEMS 2023年 279卷

作者： Cheng, Lixin Tang, Qiuhua Liu, Shengli Zhang, Liping Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China

In the mixed-model assembly job-shops, all the components are transferred in batches from the processing stage to the assembly stage to reduce transportation costs and timely satisfy the assembly material requirements. However, batch transfer in such a workshop has not received due attention in the current literature. Thus, this work addresses the mixed-model assembly job-shop scheduling problem with batch transfer via a mathematical model and an augmented simulated annealing algorithm to minimize manufacturing and transportation costs. The mathematical model takes batch transfer constraints into account and constructs the temporal and spatial links among the processing, transfer, and assembly stages. Production sequencing knowledge and batch transfer knowledge are first extracted via gene expression programming algorithm and clustering method and then implanted into the algorithmic operators in the form of rules and strategies to directly identify the promising solution space. Besides, eleven neighborhood structures including the critical component-based and batch based ones, are designed to enhance the exploitation ability. A restart mechanism considering crossover and tempering helps to get out of local optima. Experimental results indicate that by adopting the extracted knowledge, problem-specific neighborhood structures, and restart mechanism, the proposed algorithm significantly outperforms other comparison algorithms in fixing the studied problem.(c) 2023 Published by Elsevier B.V.

关键词： Mixed-model assembly job-shop scheduling Batch transfer gene expression programming Clustering Simulated annealing

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On the thermal entrance length of moderately dense gas-particle flows

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2022年 182卷 121985-121985页

作者： Beetham, S. Lattanzi, A. Capecelatro, J. Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

The dissipative nature of heat transfer relaxes thermal flows to an equilibrium state that is devoid of temperature gradients. The distance to reach an equilibrium temperature - the thermal entrance length - is a consequence of diffusion and mixing by convection. The presence of particles can modify the thermal entrance length due to interphase heat transfer and turbulence modulation by momentum coupling. In this work, Eulerian-Lagrangian simulations are utilized to probe the effect of solids heterogeneity (e.g., clustering) on the thermal entrance length. For the moderately dense systems considered here, clustering leads to a factor of 2-3 increase in the thermal entrance length, as compared to an uncorrelated (perfectly mixed) distribution of particles. The observed increase is found to be primarily due to the covariance between volume fraction and temperature fluctuations, referred to as the fluid drift temperature. Using scaling arguments and gene expression programming, closure is obtained for this term in a one-dimensional averaged two-fluid equation and is shown to be accurate under a wide range of flow conditions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Heat transfer Entrance length Particle-laden flow gene expression programming

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Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches

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MATERIALS 2022年第17期15卷 5908-5908页

作者： Amin, Muhammad Nasir Ahmad, Izaz Abbas, Asim Khan, Kaffayatullah Qadir, Muhammad Ghulam Iqbal, Mudassir Abu-Arab, Abdullah Mohammad Alabdullah, Anas Abdulalim King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan COMSATS Univ Islamabad Dept Environm Sci Abbottabad Campus Abbottabad 22060 Pakistan

This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks-normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.

关键词： fired clay bricks radiation shielding compressive strength artificial neural network gene expression programming parametric and sensitivity analysis

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A predictive model for the bond strength of near-surface-mounted FRP bonded to concrete

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COMPOSITE STRUCTURES 2021年 262卷 113618-113618页

作者： Zhang, Ruiliang Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

This paper presents two artificial intelligence techniques (e.g., gene expression programming (GEP) and random forest (RF)) for predicting the bond strength of near-surface-mounted (NSM) fiber-reinforced polymer (FRP) strips or rods bonded to concrete. Experimental data from 145 direct pullout tests collected from the literature and five parameters, namely, the bond length, FRP axial rigidity, groove depth-to-width ratio, epoxy tensile strength, and concrete compressive strength, were used to develop the GEP and RF models. A comparison was conducted between the proposed GEP and RF models and two existing empirical models, namely, Seracino's model and Zhang's model, and six statistical indices were used to evaluate the performance of these four models. The results show that the proposed GEP and RF models had higher coefficient of determination (R-2) values and lower root mean squared error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), and integral absolute error (IAE) values than the two existing empirical models. Finally, a detailed parametric study was conducted to investigate the influence of each input variable on the bond strength. The results showed that the bond strength increased with increasing bond length, FRP axial rigidity, groove depth-to-width ratio, and concrete compressive strength, while the epoxy tensile strength had little effect on the bond strength.

关键词： gene expression programming Random forest Fiber-reinforced polymer Bond strength Concrete

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Fully Automated Construction of a Deep U-Net Network Model for Medical Image Segmentation

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SENSORS AND MATERIALS 2023年第10期35卷 4633-4652页

作者： Gong, Daoqing Yang, Jiayan Gan, Xinyan Gao, Xiang Zhang, Yuanxia Guangxi Univ Chinese Med Sch Publ Hlth & Management Nanning 530299 Peoples R China Yulin Normal Univ Sch Comp & Informat Engn Nanning 537000 Peoples R China

In recent years, the use of deep learning technology for image processing has become mainstream, and the U-Net network has received widespread attention owing to its unique U-shaped structure, which has achieved excellent results in the field of image segmentation, especially in medical image segmentation. To enhance the performance of the U-Net network model and establish better U-Net design variables, in this paper, we propose a fuzzy-controlled multicellular gene expression programming algorithm to automatically build and optimize the U-Net. The algorithm creates an efficient variable-length gene code, generates chromosomes for the optimization of U-Net design variables, decodes the chromosomes to construct the U-Net model, dynamically calculates population fitness and fuzzy affiliation values, and achieves the optimal U-Net network through continuous evolution. The experimental results indicate that the proposed algorithm outperforms U-Net, Fully Convolutional Networks32, and VanillaUnet in image recognition segmentation, especially in the segmentation of COVID-19 CT medical images.

关键词： fully automatic gene expression programming U-Net Model medical images

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Novel Approach to Resilient Modulus Using Routine Subgrade Soil Properties

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INTERNATIONAL JOURNAL OF GEOMECHANICS 2014年第6期14卷 04014025-04014025页

作者： Yan, Ke-zhen Xu, Hong-bin Shen, Guang-hui Hunan Univ Dept Civil Engn Changsha 410082 Hunan Peoples R China

gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design applications. A database used for building the model was developed that contained grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in evaluation of the models developed. Two different correlations were developed using different combinations of the influencing parameters. The proposed constitutive models relate the resilient modulus of routine subgrade soils to moisture content w, dry density d, plasticity index (PI), percent passing a No. 200 sieve (P200), unconfined compressive strength Uc, deviatoric stress sigma d, and bulk stress . The results are compared with those from artificial neutral network (ANN) models. Overall, GEP models show good performance and are proven to be better than ANN models. The GEP-based design equations can be readily used for pavement design purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses.

关键词： Resilient modulus Laboratory tests Pavements Design Subgrades Soil properties Resilient modulus gene expression programming Laboratory tests Pavement design Subgrade

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