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Estimation model of leaf nitrogen content based on GEP and leaf spectral reflectance

COMPUTERS & ELECTRICAL ENGINEERING 2022年 98卷 107648-107648页

作者： Yang, Lechan Deng, Song Ma, Shouming Xiao, Fangxiong Jailing Inst Technol Dept Soft Engn Nanjing 211169 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

High-precision estimation of leaf nitrogen content is essential for monitoring and evaluating the quality of crop products. So, it is necessary to find out the bands that affect the estimation of leaf nitrogen content from the more original hyperspectral bands, which is very important to improve the accuracy of leaf nitrogen estimation. However, the calculation complexity based on multiple regression is relatively high and the multiple regression model needs to rely on prior knowledge. This paper proposes an estimation model of leaf nitrogen content based on gene expression programming (GEP) and leaf spectral reflectance. The experimental results show that compared with the other regression algorithms, the first derivative local band model obtained based on the GEP has better estimation accuracy, and the selected local band is less affected by moisture.

关键词： gene expression programming Leaf nitrogen content Regression Hyperspectral band

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Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

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CEMENT & CONCRETE COMPOSITES 2022年第0期133卷

作者： Salami, Babatunde Abiodun Iqbal, Mudassir Abdulraheem, Abdulazeez Jalal, Fazal E. Alimi, Wasiu Jamal, Arshad Tafsirojjaman, T. Liu, Yue Bardhan, Abidhan King Fand Univ Petr & Minerals Res Inst Interdisciplinary Res Ctr Construct & Bldg Mat Dhahran 31261 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar Pakistan Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra State Key Lab Ocean Engn Shanghai 200240 Peoples R China King Fand Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia King Fand Univ Petr & Minerals Coll Engn Civil & Environm Engn Dept Dammam 31261 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Engn Transportat & Traff Engn Dept POB 1982 Dammam 31451 Saudi Arabia Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Univ Southern Queensland USQ Ctr Future Mat CFM Sch Engn Toowoomba Qld 4350 Australia Univ Sci & Technol Beijing Res Inst Urbanizat & Urban Safety 30 Xueyuan Rd Beijing Peoples R China Natl Inst Technol NIT Civil Engn Dept Patna Bihar India

Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless of design challenges, artificial intelligence (AI) techniques have shown to be useful in reliably estimating desired concrete properties based on optimized mixture proportions. This study proposes AI-based models to predict the compressive strength of foamed concrete. Three novel AI approaches, namely artificial neural network (ANN), gene expression programming (GEP), and gradient boosting tree (GBT) models, were employed. The models were developed using 232 experimental results, considering easily acquired variables, such as the density of concrete, water-cement ratio and sand-cement ratio as inputs to estimate the compressive strength of foamed concrete. In training the models, 80% of the experimental data was used and the rest was used to validate the models. The optimized models were selected using their respective best hyper-parameters on trial and error basis;variable number of hidden layers, number of neurons and training algorithms were used for ANN, number of chromosomes, head size, number of genes, variable function set for the GEP and GBT employed number of trees, maximal depth and learning rate. The trained models were validated using parametric and sensitivity analyses of a simulated dataset. The prediction abilities of proposed models were evaluated using the coefficient of correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). For the validation data, empirical results from the performance evaluation revealed that GBT model (R = 0.977, MAE = 1.817 and RMSE = 2.69) has relative superior performance with highest correlation and least error in comparison with ANN (R = 0.975, MAE = 2.695 and RMSE = 3.40) and GEP (R = 0.96, MAE = 2.07 and RMSE = 2.80). The study concludes th

关键词： Foamed concrete Lightweight concrete Artificial neural network gene expression programming Gradient boosting tree Optimization

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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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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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Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

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MATERIALS 2022年第1期15卷 39页

作者： Khan, Sangeen Ali Khan, Mohsin Zafar, Adeel Javed, Muhammad Faisal Aslam, Fahid Musarat, Muhammad Ali Vatin, Nikolai Ivanovich Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Struct Engn Islamabad 44000 Pakistan CECOS Univ IT & Emerging Sci Civil Engn Dept Peshawar 25000 Pakistan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Univ Teknol PETRONAS Dept Civil & Environm Engn Bandar Seri Iskandar 32610 Malaysia Peter Great St Petersburg Polytech Univ St Petersburg 195291 Russia

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the gene expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted N-st by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for N-lg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for N-st, and 0.1000, 0.2700, and 0.1500 for N-lg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi expression Program.

关键词： concrete filled steel tube artificial neural network multi-physics model Random Forest Regression Adaptive Neuro-Fuzzy Inference System gene expression programming bearing capacity of columns

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Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

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BUILDINGS 2022年第10期12卷 1551页

作者： Nawaz, Muhammad Naqeeb Qamar, Sana Ullah Alshameri, Badee Karam, Steve Codur, Merve Kayaci Nawaz, Muhammad Muneeb Riaz, Malik Sarmad Azab, Marc Natl Univ Sci & Technol NUST Inst Civil Engn Islamabad 44000 Pakistan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Erzurum Tech Univ Fac Engn & Architecture Dept Ind Engn TR-25050 Erzurum Turkey Natl Univ Technol NUTECH Civil Engn Dept Islamabad 44000 Pakistan

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R-2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78

关键词： artificial intelligence gene expression programming liquid limit sensitivity and parametric studies

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Optimal shape design of an autonomous underwater vehicle based on multi-objective particle swarm optimization

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NATURAL COMPUTING 2020年第4期19卷 733-742页

作者： Tang, Qirong Li, Yinghao Deng, Zhenqiang Chen, Di Guo, Ruiqin Huang, Hai Tongji Univ Sch Mech Engn Lab Robot & Multibody Syst Shanghai 201804 Peoples R China Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China

Optimization of autonomous underwater vehicle's shape is usually a multi-objective optimization problem, which is essential for autonomousunderwater navigation and manipulation. To overcome the inefficiency of computational fluid dynamics software during the optimization process and thelimitations of traditional single-objective optimization, a novel strategy combining genetic expression programming and crowding distance basedmulti-objective particle swarm algorithm is presented. Its central idea is as follows, several underwater vehicle shapes are analysed to obtain their waterresistances and determine the best underwater robot shape. Shape factor of the bow and shape factor of the stern are employed as design variables, and sample points are selected by the optimal latin hypercube design. Then gene expression programming method is used to establish the surrogate model of resistance and surrounded volume. After that, the surrogate model based on the gene expression programming method is compared with that based on the surface respond method. The results show the superiority of the GEP method. Then the resistance and surrounded volume are set as two optimized variables and Pareto optimal solutions are obtained by using multi-objective particle swarm algorithm. Finally, the optimization results are compared with the hydrodynamic calculations, which shows the method proposed in the paper can greatly reduce the cost of computation and improve the efficiency of optimal shape design for underwater vehicle.

关键词： Autonomous underwater vehicle Shape optimization gene expression programming Multi-objective particle swarm optimization

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An evolutionary computing approach for reducing bias in the dynamic modulus predictive models of hot mix asphalt

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CONSTRUCTION AND BUILDING MATERIALS 2022年第0期350卷

作者： Eleyedath, Abhary Swamy, Aravind Krishna IIT Delhi Dept Civil Engn New Delhi 110016 India

Various published literature has indicated significant bias issues with dynamic modulus (|E*|) predictive models for the hot mix asphalt. This study proposes gene expression programming (GEP) based approach to reduce such bias. Bias analysis was carried out using database developed during national cooperative highway research program. GEP based predictive models for bias correction factors were developed and subsequently used in conjunction with |E*| predictive models to update |E*| values. Application of correction factors and subsequent statistical analysis showed improvement in the accuracy of predicted |E*| values. The extent of improvement (using sum of squared error) was dependent on model under consideration and ranged between 19 and 36%.

关键词： Dynamic modulus Local bias Bias correction factor gene expression programming Hot Mix Asphalt

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A realizable and scale-consistent data-driven non-linear eddy viscosity modeling framework for arbitrary regression algorithms

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INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 2022年 97卷

作者： Mandler, Hannes Weigand, Bernhard Inst Thermodynam Luft & Raumfahrt Pfaffenwaldring 31 D-70569 Stuttgart Germany

A data-driven modeling framework for non-linear eddy viscosity models is presented. In contrast to the majority of similar approaches, it splits the multivariate regression problem into a set of univariate problems and is, furthermore, independent of a specific machine learning algorithm. Instead of inferring the closure equation from high-fidelity data as a whole, the coefficients of the tensor polynomial are learned individually. The target variables are obtained from an efficient field inversion procedure by virtue of successive tensor projections. The hypotheses for each closure coefficient are then fitted separately by employing an arbitrary regression technique. Ordinary curve fitting, neural networks and gene expression programming are considered as examples. The robustness of the model against an extrapolation and the stability of the solver are promoted by coefficient limiters and a full barycentric realizability correction. Additionally, turbulent scale consistency is guaranteed by a..-corrective frozen-RANS method, which is the subject of a companion paper (Mandler and Weigand, 2022). The proposed strategy leads to models which are well suited for the application to the same class of flows they were inferred from, namely separated channel flows. As proven by an extensive extrapolation study, the resulting neuralSST model is robust against geometry and Reynolds number modifications provided the type of flow does not drastically change. It consistently outperforms both the shear stress transport (SST) and a more complex elliptic-blending model and agrees well with the reference data.

关键词： Data-driven turbulence modeling RANS closure Neural network gene expression programming Univariate regression

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The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2022年 119卷 104214-104214页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Karakus, Murat Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Curtin Univ Western Australian Sch Mines Minerals Energy & Ch Kalgoorlie WA 6430 Australia

The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEPLR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies.

关键词： Failure mechanism Strain burst Slabbing Squeezing gene expression programming Logistic regression

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