Artificial Intelligence Applied in the Concrete Durability Study

Interest in artificial intelligence (AI) in engineering research and practice has increased in recent years, especially the use of artificial neural network (ANN). The ANN has similar characteristics to biological neural networks, efficiently recognizing patterns and behaviors, suited to provide an accurate tool to map and understand the concrete degradation. This chapter presents the positive aspects of artificial neural network to model different concrete degradation mechanisms and provides a detailed procedure for ANN design. As example, the concrete carbonation depth is modeled by an ANN and the results show the its ability to map the carbonation phenomenon.

[1]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[2]  K. Kobayashi Mechanism of Carbonation of Concrete , 1990 .

[3]  B. Bruggen,et al.  Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions , 2014 .

[4]  L. Gu,et al.  Mechanical Characterizations of 3D-printed PLLA/Steel Particle Composites , 2018, Materials.

[5]  Trefor P. Williams,et al.  NEURAL NETWORKS FOR BACKCALCULATION OF MODULI FROM SASW TEST , 1995 .

[6]  Tao Ji,et al.  A concrete mix proportion design algorithm based on artificial neural networks , 2006 .

[7]  I. Can,et al.  Modeling with ANN and effect of pumice aggregate and air entrainment on the freeze–thaw durabilities of HSC , 2011 .

[8]  Meri Cvetkovska,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN CIVIL ENGINEERING , 2014 .

[9]  Anthony T. C. Goh Neural Networks for Evaluating CPT Calibration Chamber Test Data , 1995 .

[10]  W. M. Jenkins A neural network for structural re-analysis , 1999 .

[11]  Miguel Abambres,et al.  ANN-Based Fatigue Strength of Concrete under Compression , 2019, Materials.

[12]  Akbar A. Javadi,et al.  Neural network for constitutive modelling in finite element analysis , 2003 .

[13]  Prabhat Hajela,et al.  Neurobiological computational models in structural analysis and design , 1991 .

[14]  J. J. O. Andrade,et al.  Markov Chains and reliability analysis for reinforced concrete structure service life , 2014 .

[15]  Imad A. Basheer,et al.  Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils , 2000 .

[16]  Jari Puttonen,et al.  Reactive transport modelling of long-term carbonation , 2014 .

[17]  Q. Yu,et al.  Assessing the chemical involvement of limestone powder in sodium carbonate activated slag , 2017 .

[18]  İlker Bekir Topçu,et al.  Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .

[19]  Esko Sistonen,et al.  Service Life Prediction of Repaired Structures Using Concrete Recasting Method: State-of-the-Art☆ , 2013 .

[20]  Georgios E. Stavroulakis,et al.  Neural crack identification in steady state elastodynamics , 1998 .

[21]  C. Alonso,et al.  Efecto de la distancia al mar en la agresividad por cloruros en estructuras de hormigón en la costa brasileña , 2003 .

[22]  Tarek Hegazy,et al.  Neural networks as tools in construction , 1991 .

[23]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[24]  K. R. Ramakrishnan,et al.  Neural network analysis for corrosion of steel in concrete , 2005 .

[25]  L. Lam,et al.  Influence of recycled aggregate on slump and bleeding of fresh concrete , 2007 .

[26]  Jianzhuang Xiao,et al.  Using artificial neural networks to assess the applicability of recycled aggregate classification by different specifications , 2016, Materials and Structures.

[27]  Sara A. Babiker,et al.  DESIGN OPTIMIZATION OF REINFORCED CONCRETE BEAMS USING ARTIFICIAL NEURAL NETWORK , 2012 .

[28]  Abd Elmoaty M. Abd Elmoaty,et al.  Prediction of concrete compressive strength due to long term sulfate attack using neural network , 2014 .

[29]  C. Poon,et al.  Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete , 2013 .

[30]  F. Mazza,et al.  Crevice corrosion : a neural network approach , 1996 .

[31]  Koichi Maekawa,et al.  MODELING OF PH PROFILE IN PORE WATER BASED ON MASS TRANSPORT AND CHEMICAL EQUILIBRIUM THEORY , 2000 .

[32]  K. Maekawa,et al.  Multi-scale Modeling of Concrete Performance , 2003 .

[33]  Ronggui Liu,et al.  Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network , 2009, Adv. Artif. Neural Syst..

[34]  E. Possan,et al.  Modeling the Carbonation Front of Concrete Structures in the Marine Environment through ANN , 2018, IEEE Latin America Transactions.

[35]  Gholamali Shafabakhsh,et al.  Analytical evaluation of load movement on flexible pavement and selection of optimum neural network algorithm , 2015 .

[36]  Ha-Won Song,et al.  Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling , 2010 .

[37]  Charu C. Aggarwal,et al.  An Introduction to Neural Networks , 2018 .

[38]  James E. Rowings,et al.  Construction Labor Productivity Modeling with Neural Networks , 1998 .

[39]  A. Gandomi,et al.  New formulations for mechanical properties of recycled aggregate concrete using gene expression programming , 2017 .

[40]  Ayaho Miyamoto,et al.  Development of Concrete Bridge Rating Prototype Expert System with Machine Learning , 1997 .

[41]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[42]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[43]  Mohammadreza Vafaei,et al.  Real-time Seismic Damage Detection of Concrete Shear Walls Using Artificial Neural Networks , 2013 .

[44]  M. Iqbal Khan,et al.  Influence of Fiber Properties on Shear Failure of Steel Fiber Reinforced Beams Without Web Reinforcement: ANN Modeling , 2016 .

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

[46]  Miroslaw J. Skibniewski,et al.  Estimating construction productivity: neural-network-based approach , 1994 .

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

[48]  Ahmed M. Azmy,et al.  Neural networks for predicting compressive strength of structural light weight concrete , 2009 .

[49]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[50]  Manolis Papadrakakis,et al.  Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation , 1996 .

[51]  Akbar A. Javadi,et al.  Developing constitutive models from EPR‐based self‐learning finite element analysis , 2018 .

[52]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[53]  Sana Muqeem,et al.  Development of construction labor productivity estimation model using artificial neural network , 2011, 2011 National Postgraduate Conference.

[54]  H. M. Chen,et al.  Neural Network for Structural Dynamic Model Identification , 1995 .

[55]  P. K. Mehta,et al.  Concrete: Microstructure, Properties, and Materials , 2005 .

[56]  S. Masri,et al.  Application of Neural Networks for Detection of Changes in Nonlinear Systems , 2000 .

[57]  Peter E.D. Love,et al.  ANN-Based Mark-Up Estimation System with Self-Explanatory Capacities , 1999 .

[58]  Hojjat Adeli,et al.  Advances in Design Optimization , 1994 .

[59]  Walter Bogaerts,et al.  SCC Analysis of Austenitic Stainless Steels in Chloride-Bearing Water by Neural Network Techniques , 1992 .

[60]  Rogério Carrazedo,et al.  Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth , 2019, Journal of Building Pathology and Rehabilitation.

[61]  Dong Hyawn Kim Neuro-control of fixed offshore structures under earthquake , 2009 .

[62]  Robert A. Cottis,et al.  Phenomenological modelling of atmospheric corrosion using an artificial neural network , 1999 .

[63]  James L. Rogers,et al.  SIMULATING STRUCTURAL ANALYSIS WITH NEURAL NETWORK , 1994 .

[64]  Neven Ukrainczyk,et al.  A neural network method for analysing concrete durability , 2008 .

[65]  Xudong Cheng,et al.  Combined effect of carbonation and chloride ingress in concrete , 2016 .

[66]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[67]  Lei Zhang Artificial Neural Network model design and topology analysis for FPGA implementation of Lorenz chaotic generator , 2017, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).

[68]  Feng Xing,et al.  Carbonation of concrete made with dredged marine sand and its effect on chloride binding , 2016 .

[69]  Fatih Onur Hocaoglu,et al.  Modeling corrosion currents of reinforced concrete using ANN , 2009 .

[70]  C. John Yoon,et al.  Neural Network Approaches to Aid Simple Truss Design Problems , 1994 .

[71]  Michael N. Fardis,et al.  FUNDAMENTAL MODELING AND EXPERIMENTAL INVESTIGATION OF CONCRETE CARBONATION , 1991 .