A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.

[1]  Binh Thai Pham,et al.  Development of advanced artificial intelligence models for daily rainfall prediction , 2020, Atmospheric Research.

[2]  Hossein Moayedi,et al.  A spatially explicit deep learning neural network model for the prediction of landslide susceptibility , 2020 .

[3]  Panagiotis G. Asteris,et al.  Concrete compressive strength using artificial neural networks , 2019, Neural Computing and Applications.

[4]  Zaher Mundher Yaseen,et al.  Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq , 2019, Comput. Electron. Agric..

[5]  Tien-Thinh Le,et al.  Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach. , 2019, Chemosphere.

[6]  Nguyen Trung Thang,et al.  Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction , 2019, Applied Sciences.

[7]  Binh Thai Pham,et al.  Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data , 2019, Sensors.

[8]  P. G. Asteris,et al.  Application of artificial neural networks for the predictionof the compressive strength of cement-based mortars , 2019 .

[9]  Binh Thai Pham,et al.  Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam , 2019, Geocarto International.

[10]  Le,et al.  Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete , 2019, Applied Sciences.

[11]  Binh Thai Pham,et al.  Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran , 2019, Comput. Electron. Agric..

[12]  Binh Thai Pham,et al.  Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. , 2019, The Science of the total environment.

[13]  Binh Thai Pham,et al.  Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt , 2019, Applied Sciences.

[14]  Panagiotis G. Asteris,et al.  Prediction of the compressive strength of self-compactingconcrete using surrogate models , 2019 .

[15]  Khalid M. Mosalam,et al.  Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation , 2019, Comput. Aided Civ. Infrastructure Eng..

[16]  Liborio Cavaleri,et al.  Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks , 2019, Applied Sciences.

[17]  Karim Moussaceb,et al.  Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates , 2019, Construction and Building Materials.

[18]  Tien-Thinh Le,et al.  Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials , 2019, Materials.

[19]  Tuan Anh Pham,et al.  Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections , 2019, Applied Sciences.

[20]  Young Soo Yoon,et al.  Modeling the compressive strength of high-strength concrete: An extreme learning approach , 2019, Construction and Building Materials.

[21]  Binh Thai Pham,et al.  Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression , 2019, Materials.

[22]  Binh Thai Pham,et al.  Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees , 2019, Materials.

[23]  Christian Soize,et al.  Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites , 2019, Computer Methods in Applied Mechanics and Engineering.

[24]  Binh Thai Pham,et al.  Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches , 2019, Applied Sciences.

[25]  Hui Chen,et al.  Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models , 2019, Applied Sciences.

[26]  Binh Thai Pham,et al.  Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete , 2019, Materials.

[27]  Hyoungkwan Kim,et al.  Encoder–decoder network for pixel‐level road crack detection in black‐box images , 2019, Comput. Aided Civ. Infrastructure Eng..

[28]  Amin Mohebkhah,et al.  Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects , 2019, Applied Sciences.

[29]  Xiao Liang,et al.  Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization , 2018, Comput. Aided Civ. Infrastructure Eng..

[30]  Tuan Nguyen,et al.  Deep neural network with high‐order neuron for the prediction of foamed concrete strength , 2018, Comput. Aided Civ. Infrastructure Eng..

[31]  Liborio Cavaleri,et al.  Masonry Compressive Strength Prediction Using Artificial Neural Networks , 2018, TMM_CH.

[32]  Xiaochun Luo,et al.  Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning , 2018, Comput. Aided Civ. Infrastructure Eng..

[33]  Vuong Minh Le,et al.  Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams , 2019 .

[34]  Ataollah Shirzadi,et al.  Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network , 2019, Lecture Notes in Civil Engineering.

[35]  Truong Thi My Thanh,et al.  Analysis of Tourist Travel Behavior and Recommendation for Active Transport Encouragement Strategies, the Case of Hue City , 2019, Lecture Notes in Civil Engineering.

[36]  Binh Thai Pham,et al.  A Possibility of AI Application on Mode-choice Prediction of Transport Users in Hanoi , 2019, Lecture Notes in Civil Engineering.

[37]  Binh Thai Pham,et al.  Development of Artificial Neural Networks for Prediction of Compression Coefficient of Soft Soil , 2019, Lecture Notes in Civil Engineering.

[38]  Panagiotis G. Asteris,et al.  Soft computing-based techniques for concrete beams shear strength , 2019, Procedia Structural Integrity.

[39]  P. G. Asteris,et al.  Compressive strength of natural hydraulic lime mortars using soft computing techniques , 2019, Procedia Structural Integrity.

[40]  Emadaldin Mohammadi Golafshani,et al.  Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves , 2018, Journal of Cleaner Production.

[41]  Hossein Hashemi,et al.  End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management , 2018, Comput. Aided Civ. Infrastructure Eng..

[42]  Dongho Kang,et al.  Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging , 2018, Comput. Aided Civ. Infrastructure Eng..

[43]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[44]  Hung Nguyen-Xuan,et al.  A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete , 2018, Construction and Building Materials.

[45]  Yicheng Li,et al.  A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects , 2018, Comput. Aided Civ. Infrastructure Eng..

[46]  Binh Thai Pham,et al.  Prediction of shear strength of soft soil using machine learning methods , 2018, CATENA.

[47]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[48]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[49]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[50]  Hadi Meidani,et al.  Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[51]  Panagiotis G. Asteris,et al.  Surface treatment of tool steels against galling failure , 2018 .

[52]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[53]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[54]  Liborio Cavaleri,et al.  Modeling of surface roughness in electro-discharge machining using artificial neural networks , 2017 .

[55]  Panagiotis G. Asteris,et al.  Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials , 2017, Sensors.

[56]  Nebojša Vasović,et al.  Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation , 2017 .

[57]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[58]  Roman Jaskulski,et al.  Probabilistic Analysis of Shear Resistance Assured by Concrete Compression , 2017 .

[59]  Hojjat Adeli,et al.  Novel Approach for Concrete Mixture Design Using Neural Dynamics Model and Virtual Lab Concept , 2017 .

[60]  H. Khanna Nehemiah,et al.  Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets , 2016, Appl. Soft Comput..

[61]  Martin Valtierra-Rodriguez,et al.  Neurocomputing in Civil Infrastructure , 2016 .

[62]  Nhat-Duc Hoang,et al.  Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model , 2016 .

[63]  Nhat-Duc Hoang,et al.  Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression , 2016, J. Comput. Civ. Eng..

[64]  Andrey P. Jivkov,et al.  Monte Carlo Simulations of Mesoscale Fracture of Concrete with Random Aggregates and Pores: a Size Effect Study , 2015 .

[65]  Christian Soize,et al.  Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite , 2015 .

[66]  Karin K. Breitman,et al.  Uncertainty quantification through the Monte Carlo method in a cloud computing setting , 2014, Comput. Phys. Commun..

[67]  Hui Li,et al.  Robust Bayesian Compressive Sensing for Signals in Structural Health Monitoring , 2014, Comput. Aided Civ. Infrastructure Eng..

[68]  Min-Yuan Cheng,et al.  High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT) , 2014, Eng. Appl. Artif. Intell..

[69]  Sudarsana Rao hunchate,et al.  Mix Design of High Performance ConcreteUsing Silica Fume and Superplasticizer , 2014 .

[70]  Christian Soize,et al.  Stochastic framework for modeling the linear apparent behavior of complex materials: Application to random porous materials with interphases , 2013 .

[71]  Jui-Sheng Chou,et al.  Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength , 2013 .

[72]  Halil Ibrahim Erdal.  Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction , 2013, Eng. Appl. Artif. Intell..

[73]  Ersin Namli,et al.  High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform , 2013, Eng. Appl. Artif. Intell..

[74]  Mohammad S. Khorsheed,et al.  Comparative evaluation of text classification techniques using a large diverse Arabic dataset , 2013, Language Resources and Evaluation.

[75]  Alex Alexandridis,et al.  A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals , 2012 .

[76]  Ali Akbar Ramezanianpour,et al.  Practical evaluation of relationship between concrete resistivity, water penetration, rapid chloride penetration and compressive strength , 2011 .

[77]  Jui-Sheng Chou,et al.  Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques , 2011, J. Comput. Civ. Eng..

[78]  Shaul Mordechai,et al.  Applications of Monte Carlo method in science and engineering , 2011 .

[79]  K. Sathiyakumari,et al.  Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling , 2010 .

[80]  Ryan P. Adams,et al.  Slice sampling covariance hyperparameters of latent Gaussian models , 2010, NIPS.

[81]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[82]  B. V. Venkatarama Reddy,et al.  Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN , 2009 .

[83]  Hojjat Adeli,et al.  Perceptron Learning in Engineering Design , 2008 .

[84]  A. Gelfand,et al.  Gaussian predictive process models for large spatial data sets , 2008, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[85]  B. Schölkopf,et al.  Kernel methods in machine learning , 2007, math/0701907.

[86]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy polynomial neural networks for approximation of the compressive strength of concrete , 2008, Appl. Soft Comput..

[87]  Iain Murray,et al.  Introduction to Gaussian Processes , 2008 .

[88]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[89]  I-Cheng Yeh,et al.  Analysis of Strength of Concrete Using Design of Experiments and Neural Networks , 2006 .

[90]  Manish A. Kewalramani,et al.  Prediction of Concrete Strength Using Neural-Expert System , 2006 .

[91]  Murat Dicleli,et al.  Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .

[92]  I-Cheng Yeh,et al.  A Mix Proportioning Methodology for Fly Ash and Slag Concrete Using Artificial Neural Networks , 2003 .

[93]  P. Paramasivam,et al.  Correlations between Mechanical Properties of High-Strength Concrete , 2002 .

[94]  Andrew Scanlon,et al.  Fundamentals of High Performance Concrete , 2001 .

[95]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[96]  Ping-Kun Chang,et al.  Influence of mixing techniques on properties of high performance concrete , 2001 .

[97]  Wei Sun,et al.  Compressive strength and pore structure of high-performance concrete after exposure to high temperature up to 800°C , 2000 .

[98]  I-Cheng Yeh,et al.  Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming , 1999 .

[99]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[100]  I. Yeh.  Modeling Concrete Strength with Augment-Neuron Networks , 1998 .

[101]  Roger Duval,et al.  Influence of Silica Fume on the Workability and the Compressive Strength of High-Performance Concretes , 1998 .

[102]  Hojjat Adeli,et al.  A neural dynamics model for structural optimization—Theory , 1995 .

[103]  Hyo Seon Park,et al.  A neural dynamics model for structural optimization—Application to plastic design of structures , 1995 .

[104]  Hojjat Adeli,et al.  Distributed Finite-Element Analysis on Network of Workstations—Implementation and Applications , 1995 .

[105]  Hojjat Adeli,et al.  Distributed Genetic Algorithm for Structural Optimization , 1995 .

[106]  F. D. Lydon,et al.  Effect of coarse aggregate on elastic modulus and compressive strength of high performance concrete , 1995 .

[107]  S Guirguis,et al.  High performance concrete , 1993 .

[108]  N. Higham.  Analysis of the Cholesky Decomposition of a Semi-definite Matrix , 1990 .