Machine learning for predicting long-term deflections in reinforce concrete flexural structures

다국어 초록 (Multilingual Abstract)

Prediction of deflections of reinforced concrete (RC) flexural structures is vital to evaluate the workability and safety of structures during its life cycle. Empirical methods are limited to predict a long-term deflection of RC structures because they are difficult to consider all influencing factors. This study presents data-driven machine learning (ML) models to early predict the long-term deflections in RC structures. An experimental dataset was used to build and evaluate single and ensemble ML models. The models were trained and tested using the stratified 10-fold cross-validation algorithm. Analytical results revealed that the ML model is effective in predicting the deflection of RC structures with good accuracy of 0.972 in correlation coefficient (R), 8.190 mm in root mean square error (RMSE), 4.597 mm in mean absolute error (MAE), and 16.749% in mean absolute percentage error (MAPE). In performance comparison against with empirical methods, the prediction accuracy of the ML model improved significantly up to 66.41% in the RMSE and up to 82.04% in the MAE. As a contribution, this study proposed the effective ML model to facilitate designers in early forecasting long-term deflections in RC structures and evaluating their long-term serviceability and safety.

참고문헌 (Reference)

1 Chou, J. -S., "The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate" 65 : 471-483, 2017

2 Chou, J. -S., "The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate" 65 : 471-483, 2016

3 Panfilov, D. A., "The methodology for calculating deflections of reinforced concrete beams exposed to short duration uniform loading(based on nonlinear deformation model)" 91 : 188-193, 2014

4 Innofactor, "The advantages of machine learning"

5 Hall, M., "The WEKA data mining software : An update" 11 (11): 10-18, 2009

6 Wolpert, D. H., "Stacked generalization" 5 (5): 241-259, 1992

7 Rodriguez-Gutierrez, J. A., "Shortand long-term deflections in reinforced, prestressed, and composite concrete beams" 133 (133): 495-506, 2007

8 John, C. P., "Sequential minimal optimization: A fast algorithm for training support vector machines" Microsoft Research 1998

9 Ruholla Jafari-Marandi, "Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications" 한국CDE학회 4 (4): 282-304, 2017

10 Dietterich, T. G., "Proceedings of the First International Workshop on Ensemble Methods in Machine Learning, Multiple Classifier Systems" Springer 1-15, 2000

11 Rosenblatt, F. X., "Principles of neurodynamics: Perceptrons and the theory of brain mechanisms" Spartan Books 1961

12 ZP, B., "Predictions of concrete effects using age adjusted effective modulus method" 69 : 212-217, 1972

13 Jang, J., "Prediction of optimum heating timing based on artificial neural network by utilizing BEMS data" 22 : 66-74, 2019

14 Kara, I. F., "Prediction of deflection of reinforced concrete shear walls" 40 (40): 777-785, 2009

15 Bilous, I., "Parametric analysis of external and internal factors influence on building energy performance using non-linear multivariate regression models" 20 : 327-336, 2018

16 Sushil Kumar, "On the learning machine with compensatory aggregation based neurons in quaternionic domain" 한국CDE학회 6 (6): 33-48, 2019

17 Kittler, J., "On combining classifiers" 20 (20): 226-239, 1998

18 Siamak Ghadimi, "Multiple Crack Identification in Euler Beams using Extreme Learning Machine" 대한토목학회 21 (21): 389-396, 2017

19 Chou, J. -S., "Machine learning in concrete strength simulations : Multination data analytics" 73 : 771-780, 2014

20 Espion, B., "Long-term sustained loading tests on reinforced concrete beams : A selected data base" 88 (88): 88-81, 1988

21 Gribniak, V., "Long-term deflections of reinforced concrete elements : Accuracy analysis of predictions by different methods" 17 (17): 297-313, 2013

22 Mar´ı, A. R., "Long-term deflections in cracked reinforced concrete flexural members" 32 (32): 829-842, 2010

23 Alwis, W. A. M., "Long-term deflection of RC beams under constant loads" 21 : 168-175, 1999

24 Olorunniwo, A., "Long-term deflection of RC beams" 120 (120): 2220-2226, 1994

25 Bakoss, S. L., "Long term deflections of reinforced concrete beams" 121 (121): 203-212, 1982

26 Araujo, J. M., "Improvement of the ACI method for calculation of deflections of reinforced concrete beams" 7 : 49-60, 2005

27 Liang, H., "Improved estimation in multiple linear regression models with measurement error and general constraint" 100 (100): 726-741, 2009

28 Cheng, M. Y., "Highperformance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model" 28 : 106-115, 2012

29 Morris, M. D., "Factorial sampling plans for preliminary computational experiments" 33 (33): 161-174, 1991

30 Chou, J. -S., "Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength" 49 : 554-563, 2013

31 Ngo, N. -T., "Early predicting cooling loads for energyefficient design in office buildings by machine learning" 182 : 264-273, 2019

32 Tseng, T. -L., "E-quality control : A support vector machines approach" 3 (3): 91-101, 2016

33 Branson, D. E., "Deformation of concrete structures" McGraw-Hill 1977

34 ACI, C., "Deflections of reinforced concrete flexural members" 63 (63): 637-674, 1966

35 Gribniak, V., "Deflection prediction of reinforced concrete beams by design codes and computer simulation" 56 : 2175-2186, 2013

36 Gilbert, R. I., "Deflection calculation for reinforced concrete structures—why we sometimes get it wrong" 96 (96): 1027-1032, 1999

37 Bustillo, A., "Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components" 3 (3): 337-348, 2016

38 Jamil, L. S., "Data analysis based on datamining algorithms usingWekaworkbench" 5 (5): 262-267, 2016

39 Chong, A., "Continuous-time Bayesian calibration of energy models using BIM and energy data" 194 : 177-190, 2019

40 Ghali, A., "Concrete structure : Stress and deformation" J. W. Arrowsmith Ltd 1986

41 Naderpour, H., "Compressive strength prediction of environmentally friendly concrete using artificial neural networks" 16 : 213-219, 2018

42 Kuncheva, L. I., "Combining pattern classifiers : Methods and algorithms" Wiley 2014

43 Beton, C. E. -I. D, "CEB-FIP model code 1990" 1990

44 Ferrario, E., "Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems" 67 : 70-84, 2017

45 Breiman, L., "Bagging predictors" 24 (24): 123-140, 1996

46 Bonsang Koo, "Applying novelty detection to identify model element to IFC class misclassifications on architectural and infrastructure Building Information Models" 한국CDE학회 5 (5): 391-400, 2018

47 Pan, Q., "An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo simulation" 67 : 85-95, 2017

48 Erdal, H. I., "Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms" 477 : 119-128, 2013

49 Smola, A. J., "A tutorial on support vector regression" 14 (14): 199-222, 2004

50 Chang Kook Oh, "A Bayesian Learning Method for Structural Damage Assessment of Phase I IASC-ASCE Benchmark Problem" 대한토목학회 22 (22): 987-992, 2018