Data-Driven Pavement Performance Modelling: A Short Review

被引：0

作者：

Wang, Ze Zhou ^{[1
]}

Al-Tabbaa, Abir ^{[1
]}

Hakim, Bachar ^{[2
]}

Indraratna, Buddhima ^{[3
]}

机构：

[1] Univ Cambridge, Cambridge CB3 0FA, England

[2] AECOM Ltd, Dallas, England

[3] Univ Technol Sydney, Ultimo, NSW 2007, Australia

来源：

PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON TRANSPORTATION GEOTECHNICS, VOL 1, ICTG 2024 | 2025年 / 402卷

关键词：

Pavement management; Performance model; Data-driven method; PREDICTION;

D O I：

10.1007/978-981-97-8213-0_25

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

The road network in many countries holds significant value, and the amount will continue to grow due to the rapid growth of cities and populations. Given this, it is crucial to improve knowledge of the existing road assets, based on which effective management strategies can be customized to maximize the useful service life of current road assets. A key aspect in achieving this goal is the employment of performance modelling, which forecasts future pavement performance. In recent years, the increasing popularity of data-driven approaches has propelled the development of advanced pavement performance models. In this paper, existing data-driven performance models developed globally for different types of pavements and various climate and environmental conditions are first summarized. The review on data-driven performance models then focuses on the capabilities of these models: (i) in handling the time-dependent nature of the data involved, and (ii) in utilizing the existing information available to engineers to forecast future pavement conditions. The objective of this review is to highlight the current state-of-the-art and challenges in data-driven performance modelling and conclude with potential directions and insights for driving innovation and research in the roads sector for practical applications.

引用

页码：231 / 239

页数：9

共 41 条

[1] Ahmed K., 2006, INTERNATIONALJOURNAL, V7, P23
[2] Hierarchical asphalt pavement deterioration model for climate impact studies
Anyala, M.
Odoki, J. B.
Baker, C. J.
[J]. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2014, 15 (03) : 251 - 266
[3] Attoh-Okine NO, 1994, TRANSPORTATION RES B, V1
[4] Barros R, 2022, 11 INT C BEAR CAP RO, P527
[5] Five-Year Project-Level Statewide Pavement Performance Forecasting Using a Two-Stage Machine Learning Approach Based on Long Short-Term Memory
Bukharin, Alexander W.
Yang, Zhongyu
Tsai, Yichang
[J]. TRANSPORTATION RESEARCH RECORD, 2021, 2675 (11) : 280 - 290
[6] Butt AA, 1987, Pavement performance prediction model using the Markov process
[7] Development of the Road Pavement Deterioration Model Based on the Deep Learning Method
Choi, Seunghyun
Do, Myungsik
[J]. ELECTRONICS, 2020, 9 (01)
[8] Development of Pavement Distress Deterioration Prediction Models for Urban Road Network Using Genetic Programming
Chopra, Tanuj
Parida, Manoranjan
Kwatra, Naveen
Chopra, Palika
[J]. ADVANCES IN CIVIL ENGINEERING, 2018, 2018
[9] A time series analysis framework for transportation infrastructure management
Durango-Cohen, Pablo L.
[J]. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2007, 41 (05) : 493 - 505
[10] A weighted multi-output neural network model for the prediction of rigid pavement deterioration
Guo, Fengdi
Zhao, Xingang
Gregory, Jeremy
Kirchain, Randolph
[J]. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2022, 23 (08) : 2631 - 2643

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