A case study of PavMD a new tool to identify good performing pavements in New Zealand

Walt, Jacobus Daniel van der; Scheepbouwer, Eric; Pidwerbesky, B D; Guo, Brian H.W.; Ferguson, Max; Paulin, Scott

doi:10.1139/cjce-2021-0162

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The Internet of Things (also known as IoT) is a novel potential avenue to collect data to investigate this problem. IoT is an approach of using a network of tiny low-cost sensors for data collecting, analysis, real-time data visualization and real-time communication (Abu-Elkheir et al 2013, Hendricks 2015, Kim et al 2017, Van der Walt et al 2022. This study aims to extend the work by Van der Walt (2017) and improve the information available to calibrate the TVAS, for local NZ conditions (Van der Walt et al 2017, Van der Walt et al 2018.…”

Section: Introductionmentioning

confidence: 99%

“…IoT is an approach of using a network of tiny low-cost sensors for data collecting, analysis, real-time data visualization and real-time communication (Abu-Elkheir et al 2013, Hendricks 2015, Kim et al 2017, Van der Walt et al 2022. This study aims to extend the work by Van der Walt (2017) and improve the information available to calibrate the TVAS, for local NZ conditions (Van der Walt et al 2017, Van der Walt et al 2018. Specifically, the LWD on straight regional (50km/h) roads that have painted shoulder and centerline markings with no parked vehicles (Category 1 roads) versus the same road type with parked vehicles (Category 2 roads) will be investigated.…”

Section: Introductionmentioning

confidence: 99%

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An Iot Prototype to Collect Transverse Vehicle Position Data for Local Roads

Li¹,

Adams-Cook²,

Aspin³

et al. 2022

Proceedings of International Structural Engineering and Construction

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In New Zealand, most roads are chip-sealed over granular base pavements, which rely on the Transverse Variable Application Spray Bar (TVAS) during the resealing process. TVAS sprays a lower application rate of bitumen in the wheel path areas to limit flushing and uses a higher application rate outside of wheel path areas to limit raveling. Currently, this is carried out by visually identifying the wheel paths where flushing and raveling exist. This manual process is prone to human bias. Ideally, pavement resealing needs to be done before visible signs of distress appear. This study aims to automate obtaining lateral wheel path distribution (LWD) using an Internet of Things (IoT) prototype. This prototype is based on the Arduino platform, which was used to collect wheel placement distributions from vehicles on several sites in Christchurch, New Zealand. Results show that narrow roads exhibit a concentrated wheel path distribution. Additionally, vehicles tend to drive further away from the solid white shoulder line and closer together when parked cars are present. The results can be incorporated into the calibration of the TVAS and pavement design for improved construction and maintenance efficiency.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Iot Prototype to Collect Transverse Vehicle Position Data for Local Roads

Li¹,

Adams-Cook²,

Aspin³

et al. 2022

Proceedings of International Structural Engineering and Construction

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“…Rutting is a common deterioration mode of flexible pavement structures resulting from repeated load applications along the wheel paths (2)(3)(4)(5)(6)(7)(8)(9)(10). Previous research has suggested that there may be feedback loops in the causal mechanisms through which rutting and driver position (lateral wander) interrelate.…”

mentioning

confidence: 99%

Predictive Modeling for Highway Pavement Rutting: A Comparative Analysis of Auto-Machine Learning and Structural Equation Models

Ekmekci,

Sinanmis,

Woods

2023

Transportation Research Record: Journal of the Transportation R

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Highway pavements deteriorate over time as successive wheel loads cause rutting, cracking, texture loss, and so forth. Design standards and pavement performance models account for some of the known contributory factors, such as levels of traffic and vehicle composition. However, such models are limited in their predictive power, and highway authorities must conduct regular pavement condition surveys rather than relying on the standard deterioration models alone. The ways in which multiple factors affect pavement deterioration, including rutting, are complex and are believed to include feedback loops where rutting then influences driving position, exacerbating the rutting levels. Standard regression models are not well suited to representing such complex causal mechanisms. This paper compares two alternative modeling approaches, structural equation models and auto-machine learning, and evaluates the predictive ability and practicalities of each. The findings indicate that auto-machine learning (AutoML) may be superior in its predictive ability. However, the “black box” nature of AutoML results makes them potentially less useful to practitioners. A process of using machine learning to help inform a structural equation model is proposed.

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