Perancangan dan Implementasi Sistem Monitoring Beban dan Kecepatan Kendaraan Menggunakan Teknologi Weigh in Motion

Weigh in Motion (WIM) is one of the innovative solutions in traffic management that allows vehicles are weighed while on the way. In this research, it is designed a monitoring system capable of processing and calculating vehicle data in the form of vehicle load and speed through the WIM system. To support this system, the hardware used in the form of WIM sensor consists of a Load Cell, a HX711 amplifier module, and an Arduino as well as for the load signal data that has been produced by WIM system using the signal processing analysis method. Testing this system was conducted by using a passenger car at different speeds. Based on the test results, it is obtained that the WIM system is capable of measuring the running vehicle with the result of average error value for the speed is 8.94%, the distance of vehicle axle is 14.64%, and the vehicle load is 10.21%.Keywords : Weigh in Motion, Traffic Management, Load CellAbstrakWeight in Motion (WIM) merupakan salah satu solusi inovatif dalam manajemen lalu lintas yang memungkinkan kendaraan ditimbang pada saat dalam perjalanan. Pada penelitian ini dirancang sebuah sistem monitoring yang mampu mengolah dan menghitung data kendaraan berupa beban dan kecepatan kendaraan melalui sistem WIM. Untuk mendukung sistem ini digunakan perangkat keras berupa sensor WIM yang terdiri dari Load Cell, modul penguat HX711 dan Arduino serta untuk data sinyal beban yang telah dihasilkan sistem WIM menggunakan metode analisa pengolahan sinyal. Pengujian sistem ini dilakukan menggunakan sebuah mobil penumpang dengan kecepatan yang berbeda-beda. Dari hasil pengujian didapatkan sistem WIM mampu melakukan pengukuran kendaraan berjalan dengan nilai rata-rata error yang dihasilkan untuk kecepatan 8.94%, jarak sumbu kendaraan 14.64%, dan beban kendaraan 10.21%.Kata Kunci : Weigh in Motion, Manajemen Lalu Lintas, Load Cel

Reliability of a bridge with an orthotropic deck exposed to extreme traffic events

The 11th International Conference on Mathematical Methods in Reliability, HONG KONG, CHINE, 03-/06/2019 - 07/06/2019Predicting reliability levels for critical details of bridges based on limited statistical traffic data is a relevant topic nowadays. That is why the comparison between results from various statistical approaches based on the recorded data for applied traffic actions is the main point of interest of this work. The object of the current study is the famous Millau viaduct, a cable-stayed bridge with the steel orthotropic deck located in Southern France. Values of load effects that are used in analysis are derived from a finite element model of a part of the deck. They are based on data from traffic monitoring that is provided from the bridge Weigh-In-Motion system covering several months of axle loads, distances and speeds of heavy trucks. The methodology is based on a definition of limit state functions based on several statistical distributions in order to assess and compare reliability indexes for the ultimate limit state. It includes a comparison between different approaches of extreme values theory, the methodology proposed in background works for European standards and the design load model. Moreover, this work covers the influence of applied loads of a high amplitude, as global effects, onto stresses from axle loads, as local effects

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Fatigue safety monitoring and assessment of short and medium span concrete girder bridges

Concrete bridge is widely used in highway infrastructure in China, especially in short and medium span bridges. Concrete bridges are prone to fatigue failure under the coupled actions of repeated vehicles loads, environment and material degradation. In recent years, the traffic volume and vehicle weights of highway bridges have been continuously increasing, so concrete bridge fatigue problem becomes more serious. This paper introduces advanced fatigue safety monitoring techniques and fatigue performance assessment methods for short and medium span concrete girder bridges. Weigh-in-motion (WIM) system was used to record the real traffic volume, and then the acquired load spectrum was applied on typical concrete bridges through Matlab to analyze the fatigue performance of different bridge types. From the analysis results, several typical short and medium span concrete girder bridges are selected to conduct long-term service monitoring. The cross section types include hollow slab girder, T-girder and short box girder, and the structure types contain simple supported bridge and continuous girder bridge. WIM technique, dynamic strain monitoring technique and acoustic emission technique are used to monitor the key details. Fatigue performance is assessed and analyzed based on monitoring data, considering traffic increase, overload and corrosion factors

Transportation Data Research Laboratory: Data Acquisition and Archiving of Large Scaled Transportation Data, Analysis Tool Developments, and On-Line Data Support

This report contains a collection of reports for projects completed in FY 2004 and 2005 at the Transportation Data Research Laboratory (TDRL). First, an archiving technique referred to as the Unified Transportation Sensor Data Format (UTSDF), which allows simple management of large scaled Intelligent Transportation Systems (ITS) sensor generated data, is described. UTSDF was used for the development of a Data Center (DC) at TDRL. Next, data imputation algorithms to estimate missing data are presented. These algorithms were developed during the process of developing an automated on-line Automatic Traffic Recorder (ATR) and short count data system for the Office of Traffic Data & Analysis (TDA) at MnDOT. Utilizing the archived loop data, TDRL also developed a detector fault identification algorithm and software. This algorithm and test results are reported. Another project report involves cross-utilization of Road Weather Information System (RWIS) and traffic data. Several analysis approaches were developed to analyze the actual data. The analysis approaches used and findings are reported. Another project report involves development of a Weigh-in-Motion (WIM) Probe. This tool was developed as a diagnostic tool for the MnDOT's current WIM systems, and is based on a MnDOT problem statement. It is used for identification of signal anomalies and data verification. The details of this project are reported

Simplified probabilistic model for maximum traffic load from weigh-in-motion data

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Structure and infrastructure engineering on 2016, available online at: http://www.tandfonline.com/10.1080/15732479.2016.1164728This paper reviews the simplified procedure proposed by Ghosn and Sivakumar to model the maximum expected traffic load effect on highway bridges and illustrates the methodology using a set of Weigh-In-Motion (WIM) data collected on one site in the U.S.A. The paper compares different approaches for implementing the procedure and explores the effects of limitations in the site-specific data on the projected maximum live load effect for different bridge service lives. A sensitivity analysis is carried out to study changes in the final results due to variations in the parameters that define the characteristics of the WIM data and those used in the calculation of the maximum load effect. The procedure is also implemented on a set of WIM data collected in Slovenia to study the maximum load effect on existing Slovenian highway bridges and how the projected results compare to the values obtained using advanced simulation algorithms and those specified in the Eurocode of actions.Peer ReviewedPostprint (author's final draft

Mechanistic-empirical compatible traffic data generation : portable weigh-in-motion versus cluster analysis

Axle load distribution factors (ALDFs) are used as one of the primary traffic data inputs for mechanistic-empirical (ME) pavement design methods for predicting the impact of varying traffic loads on pavement performance with a higher degree of accuracy than empirical methods that are solely based on equivalent single axle load (ESAL) concept. Ideally, to ensure optimal pavement structural design, site-specific traffic load spectra data—generated from weigh-in-motion (WIM) systems—should be used during the pavement design process. However, because of the limited number of available permanent WIM stations (in Texas, for example), it is not feasible to generate a statewide ALDFs database for each highway or project from permanent WIM data. In this study, two possible alternative methods, namely, the direct measurement using a portable WIM system and the cluster analysis technique, were explored for generating site-specific ME-compatible traffic data for a highway test section, namely, state highway (SH) 7 in Bryan District (Texas). The traffic data were then used for estimating pavement performance using a ME pavement design software, namely, the Texas Mechanistic-Empirical Thickness Design System (TxME). The TxME-predicted pavement performance (e.g., rutting) using the portable WIM-generated traffic input parameters closely matched with the actual field performance. Overall, the study findings indicated that the portable WIM (with proper installation and calibration) constitutes an effective means for rapidly collecting reliable site-specific ME-compatible traffic data.https://www.astm.org/DIGITAL_LIBRARY/JOURNALS/TESTEVAL/index.html2021-05-01am2020Civil Engineerin

Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

Funder: University of CambridgeAbstractBridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.</jats:p

Marquette Interchange Phase I Final Report

This report provides details on the design, installation and monitoring of a pavement instrumentation system for the analysis of load-induced stresses and strains within a perpetual HMA pavement system. The HMA pavement was constructed as part of an urban highway improvement project in the City of Milwaukee, Wisconsin. The outer wheel path of the outside lane was instrumented with asphalt strain sensors, base and subgrade pressure sensors, subgrade moisture and temperature sensors, HMA layer temperature sensors, traffic wander strips and a weigh in motion system. Environmental sensors for air temperature, wind speed and solar radiation are also included. The system captures the pavement response from each axle loading and transmits the data through a wireless link to a resident database at Marquette University. The collected data will be used to estimate the fatigue life of the perpetual HMA pavement and to modify, as necessary, pavement design procedures used within the State of Wisconsin

Perpetual Pavement Instrumentation for the Marquette Interchange Project-Phase 1

This report provides details on the design, installation and monitoring of a pavement instrumentation system for the analysis of load-induced stresses and strains within a perpetual HMA pavement system. The HMA pavement was constructed as part of an urban highway improvement project in the City of Milwaukee, Wisconsin. The outer wheel path of the outside lane was instrumented with asphalt strain sensors, base and subgrade pressure sensors, subgrade moisture and temperature sensors, HMA layer temperature sensors, traffic wander strips and a weigh in motion system. Environmental sensors for air temperature, wind speed and solar radiation are also included. The system captures the pavement response from each axle loading and transmits the data through a wireless link to a resident database at Marquette University. The collected data will be used to estimate the fatigue life of the perpetual HMA pavement and to modify, as necessary, pavement design procedures used within the State of Wisconsin