A compressive concrete strength prediction model using artificial neural networks
A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, whe...
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TA Engineering (General) Civil engineering (General) TH Building construction Guoji, Zang A compressive concrete strength prediction model using artificial neural networks |
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A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, when the number of attributes increase or decrease, the models could not be used. Thus, a compressive concrete strength prediction model which can work with different numbers of attribute is needed. The purpose of this study is to develop compressive concrete strength prediction models using different combinations of attributes. This study includes five stages: data collection, normalization, parameters identification, model construction and evaluation. The employed data set consists of nine attributes: water, cement, fine aggregate, coarse aggregate, age, fly ash, super plasticizer, blast furnace slag and compressive concrete strength. This study produced eight prediction models where each model has different combination of attributes. It also identified appropriate weights, learning rate, momentum and number of hidden nodes for each of the proposed model, and design a general artificial neural network (ANN) architecture. Model eight of the study produced a higher correlation coefficient (i.e., 0.973) than the existing study (i.e., 0.953). This study has successfully produced eight concrete strength prediction models with good coefficient correlation. The compressive strength prediction models would benefit civil engineers as they can use the models to identify the suitability of additional materials in concrete mix. |
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Guoji, Zang |
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Guoji, Zang |
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Guoji, Zang |
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A compressive concrete strength prediction model using artificial neural networks |
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A compressive concrete strength prediction model using artificial neural networks |
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A compressive concrete strength prediction model using artificial neural networks |
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A compressive concrete strength prediction model using artificial neural networks |
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A compressive concrete strength prediction model using artificial neural networks |
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compressive concrete strength prediction model using artificial neural networks |
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Universiti Utara Malaysia |
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Awang Had Salleh Graduate School of Arts & Sciences |
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2017 |
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https://etd.uum.edu.my/6556/1/s817333_01.pdf https://etd.uum.edu.my/6556/2/s817333_02.pdf |
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my-uum-etd.65562021-05-09T03:00:34Z A compressive concrete strength prediction model using artificial neural networks 2017 Guoji, Zang Ahmad, Faudziah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences TA Engineering (General). Civil engineering (General) TH Building construction A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, when the number of attributes increase or decrease, the models could not be used. Thus, a compressive concrete strength prediction model which can work with different numbers of attribute is needed. The purpose of this study is to develop compressive concrete strength prediction models using different combinations of attributes. This study includes five stages: data collection, normalization, parameters identification, model construction and evaluation. The employed data set consists of nine attributes: water, cement, fine aggregate, coarse aggregate, age, fly ash, super plasticizer, blast furnace slag and compressive concrete strength. This study produced eight prediction models where each model has different combination of attributes. It also identified appropriate weights, learning rate, momentum and number of hidden nodes for each of the proposed model, and design a general artificial neural network (ANN) architecture. Model eight of the study produced a higher correlation coefficient (i.e., 0.973) than the existing study (i.e., 0.953). This study has successfully produced eight concrete strength prediction models with good coefficient correlation. The compressive strength prediction models would benefit civil engineers as they can use the models to identify the suitability of additional materials in concrete mix. 2017 Thesis https://etd.uum.edu.my/6556/ https://etd.uum.edu.my/6556/1/s817333_01.pdf text eng public https://etd.uum.edu.my/6556/2/s817333_02.pdf text eng public other masters Universiti Utara Malaysia Aggarwal, R., Kumar, M., Sharma, R. K., & Sharma, M. K. (2015). Predicting Compressive Strength of Concrete. International Journal of Applied Science and Engineering, (April), 171–185. Akande, K., Owolabi, T., Twaha, S., & Olatunji, S. (2014). Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete. IOSR Journal of Computer Engineering, 16(5), 88–94. http://doi.org/10.9790/0661-16518894 Alilou, V. ., & Teshnehlab, M. (2010). Prediction of 28-day compressive strength of concrete on the third day using artificial neural networks. International Journal of Engineering (IJE), (3), 565–576. Retrieved from http://www.cscjournals.org/csc/manuscript/Journals/IJE/volume3/Issue6/IJE-126.pdf Atici, U. (2011). Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems With Applications, 38(8), 9609–9618. http://doi.org/10.1016/j.eswa.2011.01.156 Betrie, G. D., Sadiq, R., Morin, K. A., & Tesfamariam, S. (2014). Uncertainty quanti fi cation and integration of machine learning techniques for predicting acid rock drainage chemistry : A probability bounds approach. Science of the Total Environment (Vol. 490). Elsevier B.V. http://doi.org/10.1016/j.scitotenv.2014.04.125 Bilim, C., Atis, C. D., Tanyildizi, H., & Karahan, O. (2009). Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software, 40, 334–340. http://doi.org/10.1016/j.advengsoft.2008.05.005 Bray, J. D., Rourke, T. D. O., Cubrinovski, M., Zupan, J. D., Taylor, M., Toprak, S., … Ballegooy, S. Van. (2013). Liquefaction Impact on Critical Infrastructure in Christchurch. Christchurch. Retrieved from http://www.cee.cornell.edu/cee/people/profile.cfm?netid=tdo1 Celikyilmaz, A., & Turksen, I. B. (2008). Enhanced fuzzy system models with improved fuzzy clustering algorithm. IEEE Transactions on Fuzzy Systems, 16(3), 779–794. http://doi.org/10.1109/TFUZZ.2007.905919 Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, Learning, and Games. (N. Cesa-Bianchi & G. Lugosi, Eds.). Barcelona: Cambridge University Press. http://doi.org/10.1017/CBO9780511546921 Chou, J., Chiu, K., Farfoura, M., & Al-Taharwa, I. (2011). Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data mining techniques. Journal of Computing in Civil Engineering, 25(3), 242–253. De Melo, V. V., & Banzhaf, W. (2016). Predicting high-performance concrete compressive strength using features constructed by kaizen programming. Proceedings - 2015 Brazilian Conference on Intelligent Systems, BRACIS 2015, 80–85. http://doi.org/10.1109/BRACIS.2015.56 Deepa, C., Sathiya Kumari, K., & Pream Sudha, V. (2010). Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling. International Journal of Comuputer Apllications, 6(5), 18–24. Doug. (2016). How to choose the number of hidden layers and nodes in a feedforward neural network? Retrieved from http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw Fred, F. (2014). What are advantages of Artificial Neural Network over Support Vector Machine. Retrieved from http://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines Ghan, Y. N., Peng, G. F., & Anson, M. (1999). Residual strength and pore structure of high-strength concrete and normal strength concrete after exposure to high temperatures, 21, 23–27. Gilan, S. S., Ali, A. M., & Ramezanianpour, A. A. (2011). Evolutionary fuzzy function with support vector regression for the prediction of concrete compressive strength. Proceedings - UKSim 5th European Modelling Symposium on Computer Modelling and Simulation, EMS 2011, 263–268. http://doi.org/10.1109/EMS.2011.28 Gupta, S. M. (2007). Support Vector Machines based Modelling of Concrete Strength. World Academy of Science, Engineering and Technology, 36(1), 305–311. Huixian, L., Housner, G. W., Lili, X., & Duxin, H. (2002). The Great Tangshan Earthquake of 1976. Technical Report: Caltech EERL.2002.001. Jang, J. S. R. (1993). ANFIS : Adaptive Network Based Fuzzy Inference System. Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665–685. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=256541 Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1), 89–93. Retrieved from http://www.ijcte.org/papers/288-L052.pdf Jordans, M., Kohrt, B., & Tol, W. (2015). Nepal Earthquakes 2015. Nepal. Retrieved from https://interagencystandingcommittee.org/system/files/20150622_nepal_earthquakes_mhpss_desk_review_150619.pdf Kabir, A., Hasan, M., & Miah, K. (2012). Predicting 28 Days Compressive Strength of Concrete from 7 Days Test Result. Advances in Design and Construction of Structures, 18–22. Kasi, K. (2015). What is difference between SVM and Neural Networks? Retrieved from https://www.quora.com/What-is-difference-between-SVM-and-Neural-Networks Kattan, M. W. (2011). Factors Affecting the Accuracy of Prediction Models Limit the Comparison of Rival Prediction Models When Applied to Separate Data Sets. European Urology, 59(4), 566–567. http://doi.org/10.1016/j.eururo.2010.11.039 Kosko, B. (1992). Neural Networks and Fuzzy Systems. Journal of Englewood Cliffs, 449. Liu, J. C., Sue, M. L., & Kou, C. H. (2009). Estimating the strength of concrete using surface rebound value and design parameters of concrete material. Tamkang Journal of Science and Engineering, 12(1), 1–7. MacKay, D. J. C. (1994). Bayesian non-linear modelling for the energy prediction competition. ASHRAE Transactions, 100, 1053–1062. Makin, J. G. (2006). Backpropagation. University of California, Berkeley, 1–8. Martinez-Molina, W., Torres-Acosta, A. A., Jauregui, J. C., Chavez-Garcia, H. L., Alonso-Guzman, E. M., Graff, M., & Arteaga-Arcos, J. C. (2014). Predicting concrete compressive strength and modulus of rupture using different NDT techniques. Advances in Materials Science and Engineering, 2014, 1–15. http://doi.org/10.1155/2014/742129 Michele, M., Raucoules, D., Lasserre, C., Pathier, E., Klinger, Y., Van der Woerd, J., … Xu, X. (2010). The M-w 7.9, 12 May 2008 Sichuan earthquake rupture measured by sub-pixel correlation of ALOS PALSAR amplitude images. Earth Planets And Space, 62(11), 875–879. http://doi.org/10.5047/eps.2009.05.002 Moriasi, D. N., Arnold, J. G., Liew, M. W. Van, Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactin of the ASABE, 50(3), 885–900. Musson, R. M. W. (2008). The seismicity of the British Isles to 1600. British. Retrieved from http://www.earthquakes.bgs.ac.uk/historical/data/studies/MUSS008/MUSS008.pdf Muthupriya, P., Subramanian, K., & Vishnuram, B. G. (2011). Prediction of Compressive Strength and Durability of High Performance Concrete By Artificial Neural Networks. International Journal of Optimization in Civil Engineering, 189–209. Nayak, S. C., Misra, B. B., & Behera, H. S. (2014). Impact of Data Normalization on Stock Index Forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6, 257–269. Nikoo, M., Torabian Moghadam, F., & Sadowski, L. (2015). Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015, 1–8. http://doi.org/10.1155/2015/849126 Noorzaei, J., Hakim, S. J. S., Jaafar, M. S., & Thanoon, W. A. M. (2007). Development of Artificial Neural Networks for Predicting Concrete Compressive Strength. International Journal of Engineering and Technology, 4(2), 141–153. Oravec, M., Petráš, M., & Pilka, F. (2008). Video Traffic Prediction Using Neural Networks. Electrical Engineering, 5(4), 59–78. Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D. (2011). Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. Internatinal Journal of Computer Theory and Engineering, 3(2), 332–337. Plagianakos, V. P., Magoulas, G. D., & Vrahatis, M. N. (2001). Leaning rate adaptation in stochastic gradient descent. In Advances in Convex Analysis and Global optimization.Springer US, 433–444. Pradhan, B., & Kundu, D. (2011). Bayes estimation and prediction of the two-parameter gamma distribution. Journal of Statistical Computation and Simulation, 81(9), 1187–1198. http://doi.org/10.1080/00949651003796335 Preetham, S., Shivaraj, M., Prema kumar, W. P., & Kumar, H. R. (2014). Support Vector Machine Technique in Analysis of Concrete-Critical Review. International Journal of Emerging Technologies and Engineering, 1(9), 199–203. Rasa, E., Ketabchi, H., & Afshar, M. H. (2009). Predicting Density and Compressive Strength of Concrete Cement Paste Containing Silica Fume Using Arti cial Neural Networks. Journal of Civil Engineering, 16(1), 33–42. Rashid, M. A., & Mansur, M. A. (2009). Considerations in producing high strength concrete. Journal of Civil Engineering, 37(1), 53–63. Sakr, G. E., Elhajj, I. H., & Mitri, G. (2011). Efficient forest fire occurrence prediction for developing countries using two weather parameters. Engineering Applications of Artificial Intelligence (Vol. 24). http://doi.org/10.1016/j.engappai.2011.02.017 Suhad, M. A., & Abbas, M. A. (2015). SUPPORT VECTOR MACHINE (SVM) FOR MODELLING THE STRENGTH OF LIGHTWEIGHT FOAMED CONCRETE. Diyala Jounal of Engineering Sciences, 29–36. Thomas, W. K. (2015). What is the difference between a Bayesian network and Bayesian neural network? Retrieved August 18, 2016, from https://www.researchgate.net/post/What_is_the_difference_between_a_Bayesian_network_and_Bayesian_neural_network Turksen, I. B. (2008). Fuzzy functions with LSE. Applied Soft Computing Journal, 8(3), 1178–1188. http://doi.org/10.1016/j.asoc.2007.12.004 Uppada, S. R., Balu, A., Gupta, A. K., & Dutta, J. R. (2014). Modeling Lipase Production From Co-cultures of Lactic Acid Bacteria Using Neural Networks and Support Vector Machine with Genetic Algorithm Optimization. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 38–43. Vakhshouri, B., & Nejadi, S. (2015). Predicition Of Compressive Strength In Light-Weight Self-Compacting Concrete By ANFIS Analytical Model. Archives of Civil Engineering, 61(2). http://doi.org/10.1515/ace-2015-0014 Vale, R. (2014). Bayesian Prediction for The Winds of Winter. Retrieved from http://arxiv.org/abs/1409.5830 Wankhade, M. W., & Kambekar, A. R. (2013). Prediction of compressive strength of concrete using Artificial Neural Network. International Journal of Scientific Research and Reviews, 2(2), 11–26. Yaqub, M., Taxila, T., Bukhari, I., & Taxila, T. (2006). Development of Mix Design for High Strength Development of Mix Design for High Strength. Conference on Our World in Concrete & Structures, 31–35. Yeh, I.-C. (1998). Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks. Journal of Civil Engineering, 28(12), 1797–1808. Retrieved from http://www.sciencedirect.com/science/article/pii/S0008884698001653 Yeh, I.-C. (2003). A Mix Proportioning Methodology for Fly Ash and Slag Concrete Using Artificial Neural Networks. Chung Hua Journal of Science and Engineering, 1(1), 77–84. Yeh, I.-C. (2006). Exploring Concrete Slump Model Using Artificial Neural Networks. Journal of Computing in Civil Engineering, 217–221. http://doi.org/10.1016/j.neunet.2008.04.002 Yeh, I. C., & Lien, L. C. (2009). Knowledge discovery of concrete material using Genetic Operation Trees. Expert Systems with Applications, 36(3 PART 2), 5807–5812. http://doi.org/10.1016/j.eswa.2008.07.004 |