Prediction of Structural Deficiency Ratio of Bridges Based on Multiway Data Factorization

Abstract

Prediction of future bridge conditions is essential in bridge management as this provides guidance for strategic planning. The National Bridge Inventory (NBI) database has been used by researchers for this purpose by utilizing Markov Chains and regression approaches. This paper introduces a hybrid method for predictions based on multiway data decomposition and simple exponential smoothing (SES). Multiway decomposition is used due to the multidimensional nature of the utilized bridge data. The simple exponential smoothing technique is utilized to predict the future proportions of structurally deficient bridges, known as the structural deficiency ratio (SD ratio) across the United States. The mean absolute error (MAE) values declined with increasing smoothing factor values, which implies bridge conditions can be reliably predicted based on recent SD ratios and not on distant past ones. With prediction metrics yielding relatively low values, this hybrid approach involving multiway data decomposition and simple exponential smoothing presents a promising tool for bridge database data analysis. individual papers. This paper is part of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, © ASCE, ISSN 2376-7642. © ASCE F4016002-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-3 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-5 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-7 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16.

Developing a Pattern Model of Damage Types on Bridge Elements Using Big Data Analytics

“…Other researchers investigated a bridge condition analysis model by analyzing bridge inspection data. They normally focused on the development of bridge condition deterioration models by applying regression and Markov models [3][4].…”