Comparative Study of Predictive Analysis Methods to Estimate Bridge Response

Abstract

Monitoring bridge performance is crucial to ensure safety and allocate resources in a cost-effective manner. This paper aims to reduce the gap between researchers and practitioners by showing how predictive analytics can be employed in the process of distilling operational information out of bridge monitoring data. Furthermore, it has the goal to aid infrastructure owners and managers in evaluating bridge performance over time and making data-driven decisions to prolong the life of the structure. To achieve this goal, the paper presents a comparative study of three predictive analysis models to estimate bridge response to heavy trucks: multilinear regression, artificial neural network, and regression tree. Following this comparison, an alternative strategy, based on the analysis of influential observations, is proposed. This approach brings together predictive power with other important capabilities such as explanatory capabilities and interpretability. The test bed structure is a short-span highway bridge which was monitored for 3 years using weigh-in-motion (traffic data) and structural health monitoring (bridge data) systems.

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References

Adeli

Yeh

Perceptron Learning in Engineering Design. Computer-Aided Civil and Infrastructure Engineering, Vol. 4, No. 4, 1989, pp. 247–256.

Ghaboussi

Garrett

J. H.

Jr.

Use of Neural Networks in Detection of Structural Damage. Computers and Structures, Vol. 42, No. 4, 1992, pp. 649–659. https://doi.org/10.1016/0045-7949(92)90132-J.

Elkordy

M. F.

Chang

K. C.

Lee

G. C.

Neural Network Trained by Analytically Simulated Damage States. Journal of Computing in Civil Engineering, Vol. 7, No. 2, 1993, pp. 130–145.

Williams

T. P.

Predicting Changes in Construction Indexes Using Neural Networks. Journal of Construction Engineering and Management, Vol. 120, No. 2, 1994, pp. 306–320.

Gagarin

Flood

Albrecht

Computing Truck Attributes with Artificial Neural Networks. Journal of Computing in Civil Engineering, Vol. 8, No. 2, 1994, pp. 179–200. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(179).

Z. B.

Shi

Z. Q.

Ososanya

E. T.

Evaluation of Bridge Conditions using Artificial Neural Networks. Proc., Southeastcon ‘96, Tampa, FL, 1996.

Masri

S. F.

Chassiakos

A. G.

Caughey

T. K.

Identification of Nonlinear Dynamic Systems using Neural Networks. ASME Journal of Applied Mechanics, Vol. 60, No. 1, 1993, pp. 123–133. https://doi.org/10.1115/1.2900734.

Cattan

Mohammadi

Analysis of Bridge Condition Rating Data using Neural Networks. Microcomputers in Civil Engineering, Vol. 12, 1997, pp. 419–429.

Tokdemir

O. B.

Ayvalik

Mohammadi

Prediction of Highway Bridge Performance by Artificial Neural Networks and Genetic Algorithms. Proc., 17th ISARC, Taipei, Taiwan, 2000.

10.

Youd

T. L.

Hansen

C. M.

Bartlett

S. F.

Revised Multilinear Regression Equations for Prediction of Lateral Spread Displacement. Journal of Geotechnical and Geoenvironmental Engineering, Vol. 128, No. 12, 2002, pp. 1007–1017. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:12(1007).

11.

Dunlop

Smith

Civil Engineering and Environmental Systems Estimating Key Characteristics of the Concrete Delivery and Placement Process using Linear Regression Analysis. Civil Engineering and Environmental Systems, Vol. 20, No. 4, 2010, pp. 273–290. https://doi.org/10.1080/1028660031000091599.

12.

Wang

Y-M.

Elhag

T. M. S.

A Comparison of Neural Network, Evidential Reasoning and Multiple Regression Analysis in Modelling Bridge Risks. Expert Systems with Application, Vol. 32, No. 2, 2007, pp. 336–348. https://doi.org/10.1016/j.eswa.2005.11.029.

13.

Elhag

T. M. S.

Wang

Y-M.

Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques. Vol. 21, No. 6, 2007, pp. 402–409.

14.

Adeli

Panakkat

A Probabilistic Neural Network for Earthquake Magnitude Prediction. Neural Networks, Vol. 22, No. 7, 2009, pp. 1018–1024. https://doi.org/10.1016/j.neunet.2009.05.003.

15.

Mata

Interpretation of Concrete Dam Behaviour with Artificial Neural Network and Multiple Linear Regression Models. Engineering Structures, Vol. 33, 2011, pp. 903–910. https://doi.org/10.1016/j.engstruct.2010.12.011.

16.

Kerh

Huang

Gunaratnam

Neural Network Approach for Analyzing Seismic Data to Identify Potentially Hazardous Bridges. Mathematical Problems in Engineering, Vol. 2011, 2011. https://doi.org/10.1155/2011/464353.

17.

Dackermann

Y-L.

Samali

Damage Identification in Civil Engineering Structures Utilizing PCA-Compressed Residual Frequency Response Functions and Neural Network Ensembles. Structural Control and Health Monitoring, Vol. 18, No. 1, 2011, pp. 207–226. https://doi.org/10.1002/stc.

18.

Son

Lim

Y. M.

A Study of the Use of Artificial Neural Networks to Estimate Dynamic Displacements Due to Dynamic Loads in Bridges. Journal of Physics: Conference Series, Vol. 382, No. 1, 2012. https://doi.org/10.1088/1742-6596/382/1/012032.

19.

Suryanita

Adnan

Application of Neural Networks in Bridge Health Prediction Based on Acceleration and Displacement Data Domain. Proceeding of the International MultiConference of Engineers and Computer Scientists, 2013, pp. 42–47.

20.

Adeli

Neural Networks in Civil Engineering 1989-2000. Computer-Aided Civil and Infrastructure Engineering, Vol. 16, No. 2, 2001, pp. 126–142.

21.

Elhag

T. M. S.

Boussabaine

A. H.

Tender Price Estimation using Artificial Neural Networks. II: Modelling. Journal of Financial Management of Property and Construction, Vol. 7, No. 1, 2002, pp. 49–64.

22.

Burke

L. I.

Introduction to Artificial Neural Systems for Pattern Recognition. Computers and Operations Research, Vol. 18, No. 2, 1991, pp. 211–220.

23.

King

S. L.

Neural Networks vs. Multiple Linear Regression for Estimating Previous Diameter. 1999. https://www.ncrs.fs.fed.us/pubs/ch/ch12/CHvolume12page159.pdf.

24.

Nguyen

Cripps

Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks. Journal of Real Estate Research, Vol. 22, No. 3, 2001, pp. 313–336.

25.

Masri

S. F.

Nakamura

Chassiakos

A. G.

Caughey

T. K.

Neural Network Approach to Detection of Changes in Structural Parameters. Journal of Engineering Mechanics, Vol. 122, No. 4, 1996, pp. 350–360. https://doi.org/10.1061/(ASCE)0733-9399(1996)122:4(350).

26.

Kumar

U. A.

Comparison of Neural Networks and Regression Analysis: A New Insight. Expert Systems with Applications, Vol. 29, No. 2, 2005, pp. 424–430. https://doi.org/10.1016/j.eswa.2005.04.034.

27.

Reich

Machine Learning Techniques for Civil Engineering Problems. Computer-Aided Civil and Infrastructure Engineering, Vol. 12, No. 4, 1997.

28.

Kutner

M. H.

Nachtsheim

C. J.

Neter

Applied Linear Statistical Models (5th ed). McGraw-Hill Higher Education, New York, NY, 2004.

29.

Stevens

J. P.

Outliers and Influential Data Points in Regression Analysis. Psychological Bulletin, Vol. 95, No. 2, 1984, pp. 334–344. https://doi.org/10.1037/0033-2909.95.2.334.

30.

Cook

R. D.

Detection of Influential Observation in Linear Regression. Technometrics, Vol. 19, No. 1, 1977, pp. 15–18. https://doi.org/10.1080/00401706.1977.10489493.

31.

Cook

R. D.

Influential Observations in Linear Regression. Journal of the American Statistical Association, Vol. 74, No. 365, 1979, pp. 169–174.

32.

Bollen

K. A.

Jackman

R. W.

Regression Diagnostics: An Expository Treatment of Outliers and Influential Cases. Sociological Methods & Research, Vol. 13, No. 4, 1985, pp. 510–542.

33.

Kosnik

D. E.

A Combined Weigh-in-Motion and Structural Health Monitoring System on a Wisconsin-Michigan Border Bridge. Proceedings of the International Conference on Weigh-in-Motion: ICWIM 6, Wiley, Hoboken, NJ, 2012, pp. 440–449.

34.

Reiff

A. J.

Sanayei

Vogel

R. M.

Statistical Bridge Damage Detection using Girder Distribution Factors. Engineering Structures, Vol. 109, 2016, pp. 139–151. https://doi.org/10.1016/j.engstruct.2015.11.006.

35.

Buckley

Basic Influence Line Equations for Continuous Beams. Journal of Structural Engineering, Vol. 123, No. 10, 1998, pp. 1416–1420.

36.

Alin

Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 3, 2010, pp. 370–374. https://doi.org/10.1002/wics.84.

37.

Browne

M. W.

Cross-Validation Methods. Journal of Mathematical Psychology, Vol. 44, No. 1088132, 2000.

38.

Sutton

C. D.

Classification and Regression Trees, Bagging, and Boosting. Handbook of Statistics, Vol. 24, No. 4, 2004, pp. 303–329. https://doi.org/10.1016/S0169-7161(04)24011-1.