Measurement of Pavement Roughness Using Android-Based Smartphone Application

Abstract

Pavement roughness is an expression of the irregularities in a pavement surface that adversely affect the ride quality of a vehicle. Roughness also affects vehicle delay costs, fuel consumption, tires, and maintenance costs. Roughness is predominantly characterized by the international roughness index (IRI), which is often measured with inertial profilers. Inertial profilers are equipped with sensitive accelerometers, a height-measuring laser, and a distance-measuring instrument for measuring vehicle vertical acceleration data and the pavement profile. Modern smartphones are equipped with several sensors including a three-axis accelerometer, which was used in this project to collect vehicle acceleration data with an Android-based application. In the study, acceleration data were double integrated numerically to obtain a pavement profile, which was input into the software program ProVAL. The pavement roughness was then calculated. For the initial validation, pavement profile and acceleration data were collected with both an inertial profiler and the newly developed smartphone application from three test sites. The initial validation results suggest that the newly developed smartphone application can measure IRI with good correspondence to the inertial profiler and with good repeatability between measurement replications. However, calibration is needed for rougher pavement sections because the current analysis techniques do not directly account for acceleration damping resulting from vehicle suspension systems. With improvements in analysis that consider the vehicle suspension effects and additional validation, the approach could be used to reduce the cost of acquiring pavement roughness data for agencies and to reduce user costs for the traveling public by providing more robust feedback about route choice and its effect on estimated vehicle maintenance cost and fuel efficiency.

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

“…and accelerating deterioration. Rough road surfaces impact the ride comfort, vehicle speed, damages to vehicle, tire wear, increased maintenance costs of vehicles and road surfaces, and number of injury and no-injury crashes at multilane highways (Islam, Buttlar, Aldunate, & Vavrik, 2014). Thus, prompt and accurate assessment of deteriorating road © 2020 Computer-Aided Civil and Infrastructure Engineering surface conditions is an essential task for the Department of Transportation (DOT) to operate the road network properly with increased service life and traffic safety (Chen, Saeed, Alqadhi, & Labi, 2019;Chen, Saeed, & Labi, 2017;Islam et al, 2014).…”

Đo đạc và phân tích chỉ số độ gồ ghề quốc tế iri của mặt đường bằng ứng dụng trên điện thoại thông minh

“…Các ứng dụng tích hợp trên điện thoại Android như "SmartRoadSense", "AndroSensor", "Roadroid" đã được sử dụng để theo dõi bề mặt đường thông qua việc tính toán chỉ số độ gồ ghề IRI từ các dữ liệu thu thập nhờ điện thoại thông minh [6,7,8,9]. Các kết quả tính toán đã cho thấy rằng điện thoại thông minh có thể đo IRI với độ chính xác chấp nhận được và có sự tương quan tốt với kết quả đo từ các máy đo độ gồ ghề theo mặt cắt khác [10,11]. Ở Việt Nam, công tác thu thập dữ liệu tình trạng mặt đường nói chung và chỉ số độ gồ ghề IRI nói riêng hiện vẫn chưa được quan tâm một cách đầy đủ, do đó việc đánh giá chất lượng mặt đường trong giai đoạn khai thác gặp nhiều khó khăn.…”

Measuring Efficiency and Effectiveness of Highway Management in Sustainability

“…IRI has score ranges of "<60, 60-94, 95-170, 171-220, and >220," and a higher value of IRI means poorer quality. We divided the ranges into "good (IRI < 95)," "fair (95 ≤ IRI ≤ 170)," and "unacceptable (IRI > 170)" according to the literature [38][39][40] and coded them as 3, 2, and 1, respectively. Then, we computed the weighted mean of each state highway by using the ratio of the certain quality road miles to the total miles: [3 × good (miles) + 2 × fair (miles) + 1 × poor (miles)]/total (miles).…”