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. 2022 Aug 31;22(17):6560.
doi: 10.3390/s22176560.

Development of a GIS-Based Methodology for the Management of Stone Pavements Using Low-Cost Sensors

Salvatore Bruno  1 , Lorenzo Vita  1 , Giuseppe Loprencipe  1
Affiliations

Development of a GIS-Based Methodology for the Management of Stone Pavements Using Low-Cost Sensors

Salvatore Bruno et al. Sensors (Basel). .

Abstract

Stone pavements are present in many cities and their historical and cultural importance is well recognized. However, there are no standard monitoring methods for this type of pavement that allow road managers to define appropriate maintenance strategies. In this study, a novel method is proposed in order to monitor the road surface conditions of stone pavements in a quick and easy way. Field tests were carried out in an Italian historic center using accelerometer sensors mounted on both a car and a bicycle. A post-processing phase of that data defined the comfort perception of the road users in terms of the awz index, as described in the ISO 2631 standard. The results derived from the dynamic surveys were also compared with the corresponding values of typical pavement indicators such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), measured only on a limited portion of the urban road network. The network's implementation in a Geographic Information System (GIS) represents the surveys' results in a graphical database. The specifications of the adopted method require that the network is divided into homogeneous sections, useful for measurement campaign planning, and adopted for the GIS' outputs representation. The comparisons between IRI-awz (R2 = 0.74) and PCI-awz (R2 = 0.96) confirmed that the proposed method can be used reliably to assess the stone pavement conditions on the whole urban road network.

Keywords: geographic information system; inertial sensor-based system; international roughness index; pavement condition index; pavement management system; pavement monitoring; ride comfort; stone pavements; urban roads.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the proposed methodology.
Figure 2
Figure 2
Road Network: (a) entire network (b) reference network.
Figure 3
Figure 3
Position form.
Figure 4
Figure 4
Distress identification menu: (a) data input with the possibility to add in situ the photo of the defect; (b) photos of the different defects specialized for stone pavements [26], to support the operator in identifying the observed defect.
Figure 5
Figure 5
Visualization of PCI results in GIS.
Figure 6
Figure 6
Visualization of IRI results in GIS.
Figure 7
Figure 7
IRI form.
Figure 8
Figure 8
Raspberry-based IMU device.
Figure 9
Figure 9
Visualization of awz results in GIS: (a) point representation (b) sample unit representation.
Figure 10
Figure 10
Typical Sampietrini Pavement in the city of Velletri.
Figure 11
Figure 11
Stone pavement network in Velletri: (a) network hierarchy (b) reference network.
Figure 12
Figure 12
GIS visualization of some sample units of the Sampietrini network in Velletri.
Figure 13
Figure 13
Installation of the prototypes on the car body: (a) position of the sensor and (b) magnetic case.
Figure 14
Figure 14
Installation of the prototypes on the bike: (a) Bike-frame and (b) Bike-helmet details.
Figure 15
Figure 15
Inspection routes.
Figure 16
Figure 16
awz car results in terms of: (a) points and (b) areas.
Figure 17
Figure 17
awz bike results: (a) bike frame results (b) bike helmet results.
Figure 18
Figure 18
awz-PCI regression considering different vehicles and sensors positions.
Figure 19
Figure 19
awz-IRI regression considering different vehicles and sensors positions.
Figure 20
Figure 20
Coefficients of variations related to the awz values calculated in the sample units surveyed in bicycle and car during the field tests.
Figure 21
Figure 21
(a) PCI and (b) IRI estimated for the surveyed network using regression models.
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References

    1. Autelitano F., Garilli E., Giuliani F. Criteria for the Selection and Design of Joints for Street Pavements in Natural Stone. Constr. Build. Mater. 2020;259:119722. doi: 10.1016/j.conbuildmat.2020.119722. - DOI
    1. Garilli E., Bruno N., Autelitano F., Roncella R., Giuliani F. Automatic Detection of Stone Pavement’s Pattern Based on UAV Photogrammetry. Autom. Constr. 2021;122:103477. doi: 10.1016/j.autcon.2020.103477. - DOI
    1. Garilli E., Autelitano F., Freddi F., Giuliani F. Urban Pedestrian Stone Pavements: Measuring Functional and Safety Requirements. Int. J. Pavement Eng. 2021:1–12. doi: 10.1080/10298436.2021.1975195. - DOI
    1. Garilli E., Giuliani F. Stone Pavement Materials and Construction Methods in Europe and North America between the 19th and 20th Century. Int. J. Archit. Herit. 2019;13:742–768. doi: 10.1080/15583058.2018.1470269. - DOI
    1. Moretti L., Cantisani G., Carpiceci M., D’Andrea A., Del Serrone G., Di Mascio P., Loprencipe G. Effect of Sampietrini Pavers on Urban Heat Islands. Int. J. Environ. Res. Public Health. 2021;18:13108. doi: 10.3390/ijerph182413108. - DOI - PMC - PubMed

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