Pavement serviceability evaluation using whole body vibration techniques: a case study for urban roads

Fuentes, Luis; Camargo, Rafael; Martínez-Arguelles, Gilberto; Komba, Julius J; Naik, Bhaven; Walubita, Lubinda F.

doi:10.1080/10298436.2019.1672872

Cited by 23 publications

(4 citation statements)

References 19 publications

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“…The accumulated suspension vertical motion The distance traveled during the test (8) where Z(X) is the longitudinal profile, K s is suspension spring rate, K t is tyre spring rate, C s is suspension damping rate, m u is unsprung mass, and m s is sprung mass. According to the Federal Highway Administration (FHWA) [59], in-service pavements can be evaluated based on the IRI value, as shown in Table 4.…”

Section: International Roughness Index (Iri)mentioning

confidence: 99%

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Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

et al. 2020

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The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

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Section: International Roughness Index (Iri)mentioning

confidence: 99%

“…IRI is a numerical index provided by the World Bank. This index is derived from dividing roughness by the longitudinal distance [5][6][7][8]. IRI is a symbol for the longitudinal roughness of roads.…”

Section: Introductionmentioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

et al. 2020

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“…A few studies have recently been published to add to the computation of the IRI on the basis of riding comfort. Fuentes et al [47] analyzed the subjective perception of the user about the level of comfort of a road pavement and the vertical accelerations through the analysis of deterministic and probabilistic models for the estimation of the pavement maintenance, specific for urban roads at low operating speed. Zhang et al [48] introduced new IRI threshold values on the basis of riding comfort; three different evaluation techniques were used: a questionnaire, a vibration acceleration method, and a psychological and physiological index root mean square of the successive differences (RMSSD) method.…”

Section: Introductionmentioning

confidence: 99%

Assessing of the Road Pavement Roughness by Means of LiDAR Technology

et al. 2020

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The assessment of the road roughness conditions plays an important role to ensure the required performances related to road safety and ride comfort, furthermore providing a tool for pavement maintenance and rehabilitation planning. In this work, the authors compared the roughness index (International Roughness Index, IRI) derived from high speed inertial profilometer with two other roughness indices, one dynamic and one geometric computed on a digital elevation model (DEM) built by using mobile laser scanner (MLS) data. The MLS data were acquired on an extra-urban road section and interpolated on the nodes of a DEM with a curvilinear abscissa, coinciding with the global navigation satellite system (GNSS) track of the profilometer. To estimate the grid cell elevation, we applied two interpolation methods, ordinary kriging (OK) and inverse distance weighting (IDW), over the same data. The roughness values computed on the surface of the DEM showed a similar trend and a high correlation with those acquired by the profilometer, higher for the dynamic index than for the geometric index. The differences between the IRI values by profilometer and those computed on the DEM were small enough not to significantly affect the judgments on the analyzed sections. Moreover, the road sub-sections derived from profilometer measure that were classified as critical coincided with those derived from light detection and ranging (LiDAR) surveys. The proposed method can be used to perform a network-level analysis. In addition, to evaluate the effects of vibrations on human comfort, we input the DEMs into a dynamic simulation software in order to compute the vertical accelerations, as specified in the UNI ISO 2631 standard. The values obtained were in line and correlated with those inferred from the standard methodology for profilometer measures.

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“…Current road ride comfort evaluation methods were established based on laboratory test results, partly because the test conditions are likely to be strictly controlled, so the results are more comparable. Evidence shows that field tests do not produce independent major vibration information for the longitudinal, lateral, vertical, roll, pitch, and yaw axes, complicating analyses [7,8]. As such, few evaluation methods have been established based on field test results, and this is increasingly becoming a problem.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of passenger comfort with road field test multi-axis vibration

Xu¹,

Chen²,

Liang³

et al. 2020

J. vibroeng.

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Using objective vibration evaluation to produce results highly consistent with real road ride comfort is challenging. The deficiencies in traditional evaluation indices, adopting an average operator, maximum operator, or cumulative operator as the main vibration information integration logic, are reported here through 19 designed road field tests in which major vibration information distribution covers all axes and vibration information is distributed in spacetime in various patterns. A new evaluation index which adopted a combination of maximum and cumulative operator is proposed to overcome these deficiencies and an interactive mechanism which standardized the process of selecting vibration information distributed among axes and spacetime is devised between the localized major vibrations. The results show that the proposed road ride comfort evaluation index is more consistent and accurate than the evaluation indices proposed by ISO 2631-1 and can be used more generally.

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Pavement serviceability evaluation using whole body vibration techniques: a case study for urban roads

Cited by 23 publications

References 19 publications

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Assessing of the Road Pavement Roughness by Means of LiDAR Technology

Evaluation of passenger comfort with road field test multi-axis vibration

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