Scholarlink

<p>This study evaluates the impact of urban drainage system on pavement roughness in Irbid City, Jordan, utilizing smartphone-based technology. Focusing on 200 arterial and collector pavement sections spanning 70.33&#xa0;km. The research integrates smartphone accelerometer data with Present Serviceability Rating (PSR) data to quantify roughness via the International Roughness Index (IRI). IRI was derived from smartphone accelerometer data using Python-based filtering and validated against IRI derived from field-measured PSR, showing strong correlation (R<sup>2</sup> = 0.85). Drainage system components (manholes and inlets) were classified into severity levels (low, medium, high) based on elevation differences relative to the surrounding pavement. Key findings revealed that drainage system significantly increases pavement roughness, with high-severity components contributing most prominently (IRI increase of 0.0298&#xa0;mm/m per percentage point, coefficient of determination (R<sup>2</sup>) = 0.363). Pavement sections with drainage system exhibited a 57% higher mean IRI (2.81&#xa0;mm/m) compared to the same sections without those (1.79&#xa0;mm/m). Regression models combining drainage system frequency and high severity level demonstrated strong predictive power (R<sup>2</sup> = 0.712). Smartphone-derived IRI values aligned closely with PSR-based results, validating this method as a scalable, low-cost alternative for pavement assessment. The study advocates prioritizing repairs of high-severity drainage system, reducing component frequency, and adopting stricter construction standards to mitigate pavement roughness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluation of Drainage System Effects on Pavement Roughness in Urban Areas Using Smartphone

  • 期刊论文

  • 发表日期：

    2026年4月27日

查看全文
  • Turki I. Obaidat,
  • Dana Abdallah Younis

摘要

This study evaluates the impact of urban drainage system on pavement roughness in Irbid City, Jordan, utilizing smartphone-based technology. Focusing on 200 arterial and collector pavement sections spanning 70.33 km. The research integrates smartphone accelerometer data with Present Serviceability Rating (PSR) data to quantify roughness via the International Roughness Index (IRI). IRI was derived from smartphone accelerometer data using Python-based filtering and validated against IRI derived from field-measured PSR, showing strong correlation (R2 = 0.85). Drainage system components (manholes and inlets) were classified into severity levels (low, medium, high) based on elevation differences relative to the surrounding pavement. Key findings revealed that drainage system significantly increases pavement roughness, with high-severity components contributing most prominently (IRI increase of 0.0298 mm/m per percentage point, coefficient of determination (R2) = 0.363). Pavement sections with drainage system exhibited a 57% higher mean IRI (2.81 mm/m) compared to the same sections without those (1.79 mm/m). Regression models combining drainage system frequency and high severity level demonstrated strong predictive power (R2 = 0.712). Smartphone-derived IRI values aligned closely with PSR-based results, validating this method as a scalable, low-cost alternative for pavement assessment. The study advocates prioritizing repairs of high-severity drainage system, reducing component frequency, and adopting stricter construction standards to mitigate pavement roughness.