Browse > Article
http://dx.doi.org/10.12989/cac.2017.20.6.645

Case-based reasoning approach to estimating the strength of sustainable concrete  

Koo, Choongwan (Department of Building Services Engineering, The Hong Kong Polytechnic University)
Jin, Ruoyu (School of Environment and Technology, University of Brighton)
Li, Bo (Department of Civil Engineering, University of Nottingham Ningbo China)
Cha, Seung Hyun (Department of Building Services Engineering, The Hong Kong Polytechnic University)
Wanatowski, Dariusz (Faculty of Engineering, University of Leeds)
Publication Information
Computers and Concrete / v.20, no.6, 2017 , pp. 645-654 More about this Journal
Abstract
Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.
Keywords
sustainable concrete; advanced case-based reasoning; environmentally friendly concrete materials; concrete mixture design; concrete strength prediction; optimization process;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 ACI (American Concrete Institute) C318-0843 (2008), Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute, Farmington Hills, Michigan, U.S.A.
2 Abd, A.M. and Abd, S.M. (2017), "Modelling the strength of lightweight foamed concrete using support vector machine (SVM)", Case Stud. Constr. Mater., 6, 8-15.   DOI
3 ACI (American Concrete Institute) 211.2-04 (2004), Standard Practice for Selecting Proportions for Structural Lightweight Concrete, American Concrete Institute, Farmington Hills, Michigan, U.S.A.
4 ASTM C127-04 (2004), Standard Test Method for Density, Relative Density (Specific Gravity), and Absorption of Coarse Aggregate, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
5 ASTM C128-07 (2007), Standard Test Method for Density, Relative Density (Specific Gravity), and Absorption of Fine Aggregate, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
6 ASTM C150-05 (2005), Standard Specification for Portland Cement, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
7 ASTM, ASTM C31/C31M-06 (2007), Standard Practice for Making and Curing Concrete Test Specimens in the Field, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
8 ASTM, ASTM C39/C39-05 (2007), Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
9 ASTM, ASTM C496/C496M-11 (2007), Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens, ASTM International, West Conshohocken, Pennsylvania, U.S.A.
10 Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38, 9609-9618.   DOI
11 Attalla, M. and Hegazy, T. (2003), "Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression", J. Constr. Eng. Manage., 129(4), 405-411.   DOI
12 Benhelal, E., Zahedi, G., Shamsaei, E. and Bahadori, A. (2013), "Global strategies and potentials to curb CO2 emissions in cement industry", J. Clean. Prod., 15, 142-161.
13 Bentz, D.P. (2010), "Powder additions to mitigate retardation in high-volume fly ash mixtures", ACI Mater. J., 107(5), 508-514.
14 Biernacki, J.J and Gottapu, M. (2015), "An advanced single-particle model for C3S hydration-validating the statistical independence of model parameters", Comput. Concrete, 15(6), 989-999.   DOI
15 Bogas, J.A., Brito, J. and Figueiredo, J.M. (2015), "Mechanical characterization of concrete produced with recycled lightweight expanded clay aggregate concrete", J. Clean. Prod., 89, 187-195.   DOI
16 Demir, F. (2005), "A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic", Cement Concrete Res., 35, 1531-1538.   DOI
17 Bondar, D., Lynsdale, C.J., Milestone, N.B., Hassani, N. and Ramezanianpour, A.A. (2011), "Engineering properties of alkali-activated natural pozzolan concrete", ACI Mater. J., 108(1), 64-72.
18 Chou, J.S., Chiu, C.K., Farfoura, M. and Al-Taharwa, I. (2011), "Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques", J. Comput. Civil Eng., 242-253.
19 Deepa, C., Sathiyakumari, K. and Sudha, V. (2010), "Prediction of the compressive strength of high performance concrete mix using tree based modelling", J. Comput. Appl. Technol., 6, 18-24.
20 Dogan, S.Z., Arditi, D., and Gunaydin, H.M. (2006), "Determining attribute weights in a CBR model for early cost prediction of structural systems", J. Constr. Eng. M., 132(10), 1092-1098.   DOI
21 Duan, Z.H. and Poon, C.S. (2014), "Factors affecting the properties of recycled concrete by using neural networks", Comput. Concrete, 14(5), 547-561.   DOI
22 Erdal, H.I., Karakurt, O. and Namli, E. (2013), "High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform", Eng. Appl. Artif. Intell., 26(4), 1246-1254.   DOI
23 Erdogan, Y.S. and Bakir, P.G. (2013), "Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem", Comput. Concrete, 11(6), 541-569.   DOI
24 Farahani, J.N., Shafigh, P., Alsubari, B., Shahnazar, S. and Mahmud, H.B. (2017), "Engineering properties of lightweight aggregate concrete containing binary and ternary blended cement", J. Clean. Prod., In Press.
25 Henry, M. and Kato, Y. (2014), "Understanding the regional context of sustainable concrete in Asia: Case studies in Mongolia and Singapore", Res. Conserv. Recycl., 82, 86-93.   DOI
26 Grist, E.R., Paine, K.A., Heath, A., Norman, J. and Pinder, H. (2015), "The environmental credentials of hydraulic lime-pozzolan concretes", J. Clean. Prod., 93, 26-37.   DOI
27 Guo, S., Dai, Q., Si, R., Sun, X. and Lu, C. (2017), "Evaluation of properties and performance of rubber-modified concrete for recycling of waste scrap tire", J. Clean. Prod., 148, 681-689.   DOI
28 Haque, M.N., Kayali, O. and Al-Khaiat, H. (2002), "Structural lightweight concrete-an environmentally responsible material of construction", Proceedings of the International Challenges of Concrete Construction Congress, Scotland, U.K.
29 Hossain, K.M.A and Lachemi, M. (2006), "Time dependent equations for the compressive strength of self-consolidating concrete through statistical optimization", Comput. Concrete, 3(4), 249-260.   DOI
30 Jin, R., Chen., Q. and Soboyejo, A. (2015), "Survey of the current status of sustainable concrete production in the U.S", Res. Conserv. Recycl., 105, 148-159.   DOI
31 Juncai, X., Qingwen, R. and Zhenzhong, S. (2015), "Prediction of the strength of concrete radiation shielding based on LS-SVM", Ann. Nucl. Energy., 85, 296-300.   DOI
32 Kandasamy, S. and Akila, P. (2015), "Experimental analysis and modeling of steel fiber reinforced SCC using central composite design", Comput. Concrete, 15(2), 215-229.   DOI
33 Koo, C., Hong, T. and Hyun. C. (2011), "The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach", Exp. Syst. Appl., 38(7), 8597-8606.   DOI
34 Langer, W.H. and Arbogast, B.F. (2002), Environmental Impact of Mining Natural Aggregate, Deposit and Geoenvironmental Models for Resource Exploitation and Environmental Security, U.S. Government, 151-170.
35 Koo, C., Hong, T. and Kim, J. (2014a), "A decision support system for determining the optimal size of a new expressway service area: Focused on the profitability", Dec. Supp. Syst., 67, 9-20.   DOI
36 Koo, C., Hong, T., Lee, M. and Park, H.S. (2013), "Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning", Environ. Sci. Technol., 47(9), 4829-4839.   DOI
37 Koo, C., Hong, T., Lee, M. and Park, H.S. (2014b), "Development of a new energy efficiency rating system for existing residential buildings", Energy Pol., 68, 218-231.
38 Limbachiya, M., Meddah, M.S. and Ouchagour, Y. (2012), "Performance of Portland/silica fume cement concrete produced with recycled concrete aggregate", ACI Mater. J., 109, 91-100.
39 Lowe, D.J., Emsley, M.W. and Harding, A. (2006), "Predicting construction cost using multiple regression techniques", J. Constr. Eng. Manage., 132(7), 750-758.   DOI
40 Mastali, M., Dalvand, A. and Fakharifar, M. (2016), "Statistical variations in the impact resistance and mechanical properties of polypropylene fiber reinforced self-compacting concrete", Comput. Concrete, 18(1), 113-137.   DOI
41 Mohammadhosseini, H. and Yatim, J.M. (2017a), "Microstructure and residual properties of green concrete composites incorporating waste carpet fibers and palm oil fuel ash at elevated temperatures", J. Clean. Prod., 144, 8-21.   DOI
42 Omran, B.A., Chen, Q. and Jin, R. (2016), "Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete", J. Comput. Civil Eng., 30(6).
43 Mohammadhosseini, H., Yatim, J.M., Sam, A.R.M. and Abdul Awal, A.S.M. (2017b), "Durability performance of green concrete composites containing waste carpet fibers and palm oil fuel ash", J. Clean. Prod., 144, 448-458.   DOI
44 Muller, M. and Wiederhold, E. (2002), "Applying decision tree methodology for rules extraction under cognitive constraints", Eur. J. Oper. Res., 136, 282-289.   DOI
45 Ni, H. and Wang, J. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30, 1245-1250.   DOI
46 Phaobunjong, K. (2002), "Parametric cost estimating model for conceptual cost estimating of building construction projects", Ph.D. Dissertation, University of Texas, Austin, Texas, U.S.A.
47 Rifat, S. (2004), "Conceptual cost estimation of building projects with regression analysis and neural networks", Can. J. Civil Eng., 31(2), 677-683.   DOI
48 Saridemir, M., Topcu, I.B., Ozcan, F. and Severcan, M.H. (2009), "Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Constr. Build. Mater., 23, 1279-1286.   DOI
49 Shafigh, P., Nomeli, M.A., Alengaram, U.J., Mahmud, H.B. and Jumaat, M.Z. (2016), "Engineering properties of lightweight aggregate concrete containing limestone powder and high-volume fly ash", J. Clean. Prod., 135, 148-157.   DOI
50 Sheng, O.R.L., Wei, C.P., Hu, P.J.H. and Chang, N. (2000), "Automated learning of patient image retrieval knowledge: Neural networks versus inductive decision trees", Dec. Supp. Syst., 30, 105-124.   DOI
51 Wang, H.Y., Hsiao, D.H. and Wang, S.Y. (2012), "Properties of recycled green building materials applied in lightweight aggregate concrete", Comput. Concrete, 10(2), 95-104.   DOI
52 Tapali, J.G., Demis, S. and Papadakis, V.G. (2013). "Sustainable concrete mix design for a target strength and service life", Comput. Concrete, 12(6), 755-774.   DOI
53 Topcu, I.B. and Boga, A.R. (2010), "Effect of boron waste on the properties of mortar and concrete", Waste. Manage. Res., 28, 626-633.   DOI
54 Valipour, M., Shekarchi, M. and Arezoumandi, M. (2017), "Chlorine diffusion resistivity of sustainable green concrete in harsh marine environments", J. Clean. Prod., 142, 4092-4100.   DOI
55 Xiao, J., Li, L., Shen, L. and Poon, C.S. (2015), "Compressive behaviour of recycled aggregate concrete under impact loading", Cement Concrete Res., 71, 46-55.   DOI
56 Yan, K., Xu, H., Shen, G. and Liu, P. (2013), "Prediction of splitting tensile strength from cylinder compressive strength of concrete by support vector machine", Adv. Mater. Sci. Eng., 1-13.
57 Yang, E.I., Yi, S.T. and Leem, Y.M. (2005), "Effect of oyster shell substituted for fine aggregate on concrete characteristics: Part I. Fundamental properties", Cement Concrete Res., 35, 2175-2182.   DOI
58 Yang, K.H., Jung, Y.B., Cho, M.S. and Tae, S.H. (2015), "Effect of supplementary cementitious materials on reduction of CO2 emissions from concrete", J. Clean. Prod., 103, 774-783.   DOI
59 Yeh, I.C. (1998), "Modeling of strength of high performance concrete using artificial neural networks", Cement Concrete Res., 28, 1797-1808.   DOI