Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

Chen, Shong-Loong; Lin, Chia-Hung; Tang, Chao-Wei; Chu, Liang-Pin; Cheng, Chiu-Kuei

doi:10.3390/su122410536

Cited by 15 publications

(14 citation statements)

References 22 publications

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“…In [21], the spaceborne L-band ALOS-PALSAR datasets with 7 m spatial resolution were used to map the relationship between the SAR backscatter values and the international roughness index (IRI) values of the roads in Thailand. The IRI is a widely used parameter to indicate the unevenness of the road surface [22]. A similar study can be found in [23], where the spaceborne X-band Cosmo-SkyMed SAR datasets with 3 m spatial resolution were used to derive the IRI of the roads in the Commonwealth of Virginia, USA.…”

Section: Introductionmentioning

confidence: 77%

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Rischioni

Babu

Baumgartner

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Airborne Synthetic Aperture Radar (SAR) has the potential to monitor remotely the road traffic infrastructure on a large scale. Of particular interest is the road surface roughness, which is an important road safety parameter. For this task, novel algorithms need to be developed. Machine learning approaches, such as Artificial Neural Networks (ANN) and Random Forest Regression, which can perform non-linear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with DLR's airborne F-SAR radar system. Several machine learning-based approaches were tested on the datasets to estimate road surface roughness. The resulting models were then compared with ground truth surface roughness values and also with the semi-empirical surface roughness model studied in previous work.

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Section: Introductionmentioning

confidence: 77%

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Rischioni

Babu

Baumgartner

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It is defined as "the accumulated suspension vertical motion divided by the distance traveled as obtained from a mathematical model of a simulated quarter-car traversing a measured profile at 80 km/h" [25]. This index is the basic tool for assessing the quality of the pavement after construction, during use and after modernization [26][27][28][29][30]. The guidelines for calculating the IRI are described in the documents [31,32].…”

Section: Measurement Methods Used To Assess Mining Influencesmentioning

confidence: 99%

Assessment of Visual Representation Methods of Linear Discontinuous Deformation Zones in the Right-of-Way

Wróblewska

Grygierek

2022

Applied Sciences

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Mining impacts on the ground surface may manifest themselves, inter alia, in the form of continuous and discontinuous deformations. Discontinuous deformations significantly deteriorate the condition of the road surface, posing a threat to the safety of use. Therefore, in mining areas, it is necessary to carry out an ongoing inspection of the condition of objects and thus determine the need and scope of their repair. The key method of assessing the scope of the surface repair needed is to observe the road surface. This article presents the results of selected measurement methods carried out near discontinuous deformation zones in the right-of-way. The use of measurements with the use of a laser profilograph allowed determining the evenness of the tested pavement. Additionally, geodetic methods were used: leveling and GPS satellite positioning, to determine the displacement of points forming the observation lines along the right-of-way. In order to identify, in detail, linear discontinuous deformations, non-ceiling photogrammetry was used with the use of an unmanned aerial vehicle. The comparison of selected methods and their advantages and disadvantages allowed for the assessment of their usefulness in visual representation zones of linear discontinuous deformations. The results of this research show the usefulness of laser profilographs, while the greatest detail of the measurement was obtained by low-ceiling photogrammetry with the use of an unmanned aerial vehicle.

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“…Some of the most commonly used indicators for measuring pavement roughness include the International Roughness Index (IRI), Profile Index (PI), Maysmeter Index (MI), Root mean Square Vertical acceleration (RMSVA), y Ride Number (RN), etc. (Chen et al, 2020). The most internationally used method is the IRI, which was developed by the World Bank during the "International Road Roughness Experiment" (IRRE) carried out in Brazil in 1982 (Sayers et al, 1986).…”

Section: Proposed Bymentioning

confidence: 99%

“…The most internationally used method is the IRI, which was developed by the World Bank during the "International Road Roughness Experiment" (IRRE) carried out in Brazil in 1982 (Sayers et al, 1986). The IRI is a mathematical model that simulates the suspension and mass of a typical vehicle traveling along a stretch of road at a reference speed, typically 80 km/h (50 mph) (Bridgelall et al, 2016), (Chen et al, 2020). This model is known as "Quater Car Simulation" (QCS) since it represents the quarter of a four-wheeled vehicle.…”

Section: Proposed Bymentioning

confidence: 99%

Probabilistic Model for Prediction of International Roughness Index Based on Monte Carlo

Moreno

Marín-Uribe

Restrepo-Tamayo

2022

ric

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The IRI International Regularity Index is a performance indicator that evaluates the functional condition of a pavement structure. Its value is a key input for the management of road assets, allowing to establish the opportune moment for carrying out interventions on the pavement. In addition, it is used to receive road surfaces, assess vehicle operating costs, evaluate the profitability of road projects and establish the cash flow in the financial administration of the project. The IRI data obtained from measurements carried out in the field, feed the deterministic deterioration model that allows future estimations of the indicator and the development of pavement maintenance programs. This research proposes to evaluate in a probabilistic way the model of the IRI International Regularity Index of the HDM-4 program, by assigning probability density functions to the input variables from real data taken in the field. To achieve this objective, a Montecarlo-type simulation model was developed, where roads must be classified by their geographical location, structural capacity of the pavement and traffic intensity expressed in Number of Equivalent Axes. The research results provide the IRI characterized by probability density functions, allowing its estimation from an expected reliability value.

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Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

Cited by 15 publications

References 22 publications

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Assessment of Visual Representation Methods of Linear Discontinuous Deformation Zones in the Right-of-Way

Probabilistic Model for Prediction of International Roughness Index Based on Monte Carlo

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