Implementation of AI for The Prediction of Failures of Reinforced Concrete Frames

Document Type : Original Research Article

Authors

Department of Civil Engineering, Faculty of Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

Abstract

Reinforced concrete tall building failure, in residual areas, can cause catastrophic disaster if they can’t survive during the destructive earthquakes. Hence, determining the damage of these buildings in the earthquake and detecting the probable mechanism formation are necessary for insurance purposes in urban areas. This paper aims to determine the failure modes of the moment resisting concrete frames (MRFs) according to the damage of the beam and column.  To achieve this goal, a 15-storey moment resisting reinforced concrete frame is modeled via IDARC software, and nonlinear dynamic time history analysis is performed through 60 seismic accelerograms. Then the collapse and non-collapse vectors are constructed obtaining the results of dynamic analysis in both modes. The artificial neural network is used for the classification of the obtained modes. The results show good agreement in failures classes. Hence it is possible to introduce the simple weight factor for frame status identification.

Keywords

Main Subjects


[1]     B.J. Alsulayfani, and T.E. Saaed, “Effect of Dynamic Analysis and Modal Combinations on Structural Design of Irregular High Rise Steel Buildings”, Asian Journal of Applied Sciences, vol. 2, no.4, p. 348-362,2009.
[2]     American Concrete Institute (ACI)). “Building code requirements for structural concrete and commentary” ACI 318-02/ACI 318R-02, Farmington Hills, MI,2002.
[3]     A. Adedeji and S.P. Ige, “Comparative Study of Seismic Analysis for Reinforced Concrete Frame Infilled with Masonry and Shape Memory Alloy Wire”, Trends in Applied Sciences Research, vol.6, no.5, p. 426-437,2011.
[4]     D. CFeng, Z. T. Liu, X. D. Wang, Z. M. Jiang, & S. X. Liang, “Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm”, Advanced Engineering Informatics, vol.45, 1011262020
[5]     A. Mehrabi Moghadam, A. Yazdani, and S. Motaghed, “Considering the Yielding Displacement Uncertainty in Reliability of Mid-Rise RC Structures”, Journal of Rehabilitation in Civil Engineering, vol.10, no. 3, p. 141-157, 2022.
[6]     M. A. H. Mirdad, Y. H. Chui, and D. Tomlinson, “Capacity and Failure-Mode Prediction of Mass Timber Panel–Concrete Composite Floor System with Mechanical Connectors”, Journal of Structural Engineering, vol. 147, no. 2, p. 1-16,‏ 2021.
[7]     S. Motaghed, andA. R. Fakhriyat, “Modeling inelastic behavior of RC adhered shear wall‏ s in opensees”, Journal of Modeling in Engineering, vol. 18, no. 63, p.15-25, ‏ 2021.
[8]     E. B. Tirkolaee, I. Mahdavi, M. M. S.Esfahani, and G. W. Weber, “A robust green location-allocation-inventory problem to design an urban waste management system under uncertainty”, Waste Management, vol. 102, no. 1, p. 340-350,2020.
[9]     C. B. Haselton, A. B. Liel, G. G. Deierlein, B. S. Dean, andJ. H. Chou, “Seismic collapse safety of reinforced concrete buildings. I: Assessment of ductile moment frames”, Journal of Structural Engineering, vol. 137, no. 4, p. 481-491,2011.
[10]  C. A. Goulet, C. B. Haselton, J. MitraniReiser, J. L. Beck, G. G. Deierlein, K. A. Porter, and J. P. Stewart, “Evaluation of the seismic performance of a codeconforming reinforcedconcrete frame building—from seismic hazard to collapse safety and economic losses”, Earthquake Engineering & Structural Dynamics, vol. 36, no. 13, p. 1973-1997, 2007.‏
[11]  S. Mangalathu and J. S. Jeon, “Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques”, Engineering Structures, vol. 160, no. 1, p. 85-94, 2018.
[12]  H. Q. Nguyen, H. B. Ly, V. Q. Tran, T. A. Nguyen, T. T. Le and B. T. Pham, “Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression”, Materials, vol. 13, no. 5, p. 1205, 2020.‏
[13]  H. B. Ly, T. T. Le, H. L. T. Vu, V. Q. Tran, L. M. Le, and B. T. Pham, “Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams”, Sustainability, vol. 12, no. 7, p. 2709. ‏2020.
[14]  A. De Stefano, D. Sabia and L. Sabia, “Probabilistic neural networks for seismic damage mechanismsprediction”, Earthquake Engineering & Structural Dynamics, vol. 28, no. 8, p. 807–821,1999.
[15]  SA. Allali, M. Abed and A. Mebarki, “Post-earthquake assessment of buildings damage using fuzzy logic”, Engineering Structures, vol. 166, p. 117–127, 2018.
[16]  P.F. Alvanitopoulos, I. Andreadis and A. Elenas, “Neuro-fuzzy techniques for the classification of earthquake damages in buildings”, Measurement, vol. 43, no. 6, p.797–809, 2010.
[17]  M.L. Carren˜o, O.D. Cardona and A.H. Barbat, “Computational tool for post-earthquake evaluation of damage in buildings”, Earthquake Spectra, vol. 26, no. 1, p. 63–86, 2010.
[18]  K. Demartinos and S. Dritsos, “First-level pre-earthquake assessment of buildings using fuzzy logic”, Earthquake Spectra, vol. 22, no. 4, p. 865–885, 2006.
[19]  E. Elwood and R.B. Corotis, “Application of fuzzy pattern recognition of seismic damage to concrete structures”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 1, no. 4, 04015011, 2015.
[20]   Silva MS and Garcia L, “Earthquake damage assessment based on fuzzy logic and neural networks”, Earthquake Spectra, vol. 17, no. 1, p.89–112, 2001.
[21]  S. Mangalathu and H.V. Burton, “Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions”, International Journal of Disaster Risk Reduction 36: 101111,2019.
[22]  S. German, I, Brilakis and R. Desroches, “Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments”, Advanced Engineering Informatics, vol. 26, no. 4, p. 846–858, 2012.
[23]  S. German, J-S. Jeon, Z. Zhu, C. Bearman, I. Brilakis, R. DesRoches and L. Lowes, “Machine vision-enhanced postearthquake inspection”, Journal of Computing in Civil Engineering, vol. 27, no. 6, p. 622–634, 2013.
[24]  S.G. Paal, J-S. Jeon, I. Brilakis and R. Des Roches, “Automated damage index estimation of reinforced concrete columns for post-earthquake evaluations”, Journal of Structural Engineering, vol. 141, no. 9, 04014228,2014.
[25]  L.Gong, C. Wang, F. Wu, J. Zhang, H. Zhang and Q. Li,“ Earthquake-induced building damage detection with post-event sub-meter VHR TerraSAR-X staring spotlight imagery”, Remote Sensing, vol. 8, no. 11, p. 1–21, 2016.
[26]  M.Peyk-Herfeh and A. Shahbahrami, “Evaluation of adaptive boosting and neural network in earthquake damage levels detection”, International Journal of Computer Applications, vol. 100, no. 3, p. 23–29, 2014.
[27]  Y. Gao and K.M. Mosalam, “Deep transfer learning for image-based structural damage recognition”, Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 9, p. 748–768, 2018.
[28]  C.S. Huang, S.L. Hung, C.M. Wen and T.T. Tu, “A neural network approach for structural identification and diagnosis of a building from seismic response data”, Earthquake Engineering and Structural Dynamics, vol. 32, no. 2, p. 187–206, 2003.
[29]  M.Nakamura, S.F. Masri, A.G. Chassiakos and T.K. Cau Ghey, “A method for non-parametric damage detection through the use of neural networks”, Earthquake Engineering and Structural Dynamics, vol. 27, no. 9, p. 997–1010, 1998.
[30]  R.O. De Lautour and P. Omenzetter, “Damage classification and estimation in experimental structures using time series analysis and pattern recognition”, Mechanical Systems and Signal Processing, vol. 24, no. 5, p. 1556–1569, 2010.
[31]  M.P. Gonza´lez and J.L. Zapico, “Seismic damage identification in buildings using neural networks and modal data”, Computers and Structures, vol. 86, no. 3–5, p. 416–426, 2008.
[32]  X. Jiang and H. Adeli, “Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings”, International Journal for Numerical Methods in Engineering, vol. 71, no.p. 606–629, 2007.
[33]  X.Jiang and H. Adeli, “Dynamic fuzzy wavelet neuroemulator for non-linear control of irregular building structures”, International Journal for Numerical Methods in Engineering, vol. 74, no. 7, p. 1045–1066, 2008.
[34]  X. Jiang and H. Adeli, “Neuro-genetic algorithm for non-linear active control of structures”, International Journal for Numerical Methods in Engineering, vol. 75, no. ,p. 770–786,2008.
[35]  X.Jiang and S. Mahadevan, “Bayesian probabilistic inference for nonparametric damage detection of structures”, Journal of Engineering Mechanics, vol. 134, no. 10, p. 820–831,2008.
[36]  Z. Wu, B. Xu and K. Yokoyama, “Decentralized parametric damage detection based on neural networks”, Computer-Aided Civil and Infrastructure Engineering, vol. 17, no. 3, p.175–184, 2002.
[37]  Xu B, Wu Z, Yokoyama K, Harada T and Chen G, “A soft post-earthquake damage identification methodology using vibration time series”, Smart Materials and Structures, vol. 14, no. 3, p. 116–124, 2005.
[38]  G.L. Molas and F. Yamazaki, “Neural networks for quick earthquake damage estimation”, Earthquake Engineering & Structural Dynamics, vol. 24, no.4, p.505–516. 1995.
[39]  T. Aoki, R. Ceravolo, A. De Stefano, C. Genovese and D. Sabia, “Seismic vulnerability assessment of chemical plants through probabilistic neural networks”, Reliability Engineering and System Safety, vol. 77, no. 3, p. 263–268, 2002.
[40]  R.O. De Lautour and P. Omenzetter, “Damage classification and estimation in experimental structures using time series analysis and pattern recognition”, Mechanical Systems and Signal Processing, vol. 24, no. 5, p. 1556–1569,2010.
[41]  K. Morfidis and K. Kostinakis, “Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks”, Advances in Engineering Software, vol. 106, p.1–16, 2017.
[42]  K. Morfidis and K. Kostinakis, “Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks”, Engineering Structures, vol. 165, no. 4, p. 120–141, 2018.
[43]  M.Vafaei, A.B. Adnan and A.B.A. Rahman, “Real-time seismic damage detection of concrete shear walls using artificial neural networks”, Journal of Earthquake Engineering, vol. 17, 1137–154, 2013.
[44]  M.Vafaei, A.B. Adnan and A.B.A. Rahman, “Aneuro-wavelet technique for seismic damage identification of cantilever structures”, Structure and Infrastructure Engineering, vol. 10, no. 12, p.1666–1684, 2014.
[45]  A. Kia and S. Sensoy, “Classification of earthquake-induced damage for R/C slab column frames using multiclass SVM and its combination with MLP neural network”, Mathematical Problems in Engineering, vol. 2014, no. 1, p. 1-14, 2014.
[46]  H.V. Burton, S. Sreekumar, M. Sharma and H. Sun, “Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators”, Structural Safety, vol. 68, no. 2, p. 85–96, 2017.
[47]  Y. Zhang and H.V. Burton, “Pattern recognition approach to assess the residual structural capacity of damaged tall buildings. Structural Safety’’, vol. 78, p. 12–22, 2019.
[48]  Y. Zhang, H.V. Burton, H. Sun and M. Shokrabadi, “A machine learning framework for assessing post-earthquake structural safety”, Structural Safety, vol. 72, no. 2, p. 1–16, 2018.
[49]  Shahidzadeh M.S., A. Amani and S. Motaghed, 2011, “FRP-Steel Relation in Circular Columns to Make an Equal Confinement”, Journal of Applied Sciences, 11: 778-787.
[50]  A.M. Reinhorn, S.K. Kunnath and R. Valles-Mattox, “IDARC 2D version 6.1: users manual”, State University of New York at Buffalo: Department of Civil Engineering, 2006.
[51]  M.A.  Alamand M.Z. Jumaat, “Eliminating Premature End Peeling of Flexurally Strengthened Reinforced Concrete Beams”, Journal of Applied Sciences, vol. 9, no. 6, p. 1106-1113,2009.
[52]  A.T. Gilmore, J.O. Jirsa, “the concept of cumulative ductility strength spectra and its use within performance- based seismic design”, ISET Journal of Earthquake Technology, vol. 41, no. 1, p. 183-200, 2004.
[53]  O. E. Kafrawy and A. Bagchi, “Computer Aided Design and Analysis of Reinforced Concrete Frame Buildings for Seismic Forces”, Information Technology Journal, vol. 6, no. 6, p. 798-808, 2007.
[54]  S. Chatterjee, S. Sarkar, S. Hore, N. Dey, A.S. Ashour and V. E. Balas, “Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings”, Neural Computing and Applications, vol. 28, no. 8, p. 2005-2016, 2017.‏
[55]  B.Z. Dehkordi, R. Abdipour, S. Motaghed, A.K. Charkh, H. Sina and M. S. ShahidZad, “Reinforced concrete frame failure prediction using neural network algorithm”, Journal of Applied Sciences, vol. 12, no. 5, p. 498-501, 2012.‏
[56]  P. Hait, A. Sil, and S. Choudhury, “Seismic damage assessment and prediction using artificial neural network of RC building considering irregularities”, Journal of Structural Integrity and Maintenance, vol. 5, no. 1, p. 51-69,2020.‏
[57]  O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed and H. Arshad, “State-of-the-art in artificial neural network applications: A survey”, Heliyon, vol. 4, no. 11, e00938, 2018.
[58]  J. Zou, Y. Han, andS.S. So, “Overview of artificial neural networks”, Artificial Neural Networks, vol. 14, no. 22, 2008.‏
[59]  S. Alagundi, andT.Palanisamy, “Prediction of Joint Shear Strength of RC Beam-Column Joint Subjected to Seismic loading using Artificial Neural Network”, Sustainability, Agri, Food and Environmental Research, vol. 10, no. 1,‏ 2022.