The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches

SMARTI - Sustainable Multi-functional Automated Resilient Transport Infrastructure

The world’s transport network has developed over thousands of years; emerging from the need of allowing more comfortable trips to roman soldiers to the modern smooth roads enabling modern vehicles to travel at high speed and to allow heavy airplanes to take off and land safely. However, in the last two decades the world is changing very fast in terms of population growth, mobility and business trades creating greater traffic volumes and demand for minimal disruption to users, but also challenges, such as climate change and more extreme weather events. At the same time, technology development to allow a more sustainable transport sector continue apace. It is within this environment and in close consultation with key stakeholders, that this consortium developed the vision to achieve the paradigm shift to Sustainable Multifunctional Automated and Resilient Transport Infrastructures. SMARTI ETN is a training-through-research programme that empowered Europe by forming a new generation of multi-disciplinary professionals able to conceive the future of transport infrastructures and this Special Issue is a collection of some of the scientific work carried out within this context. Enjoy the read

Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review

Abstract Early, effective and continuous monitoring allows to reduce costs and to extend life of road infrastructure. For this reason, over the years, more and more efforts have been made to implement more advanced and effective monitoring systems at ever more contained costs, going from impractical manual and destructive methods through automated in vehicle equipment to the most recent wireless sensor network (WSN) embedded into the pavement. The purpose of this paper is to provide a comprehensive, up-to-date critical literature review of wireless sensor networks for pavement health monitoring, considering, also, the experience gained for wired sensor as fundamental point of reference. This work presents both the methodology used to collect and analyse the current bibliography and provides a description and comments fundamental characteristics of wireless sensor networks for pavement monitoring for damage detection purposes, among which energy supply, the detection method, the hardware and network architecture and the performance validation procedures. A brief analysis of other possible complementary applications of smart sensor networks, such as traffic and surface condition monitoring, is provided. Finally, a comment is provided on the gaps and possible directions that future research could follow to allow the extensive use of wireless sensor networks for pavement health condition monitoring

Smart airport pavement instrumentation and health monitoring

Realistic characterization of pavement layer properties and responses under in-situ field conditions is critical for accurate airport pavement life predictions, planning pavement management activities as well as for calibration and validation of mechanistic-based pavement response prediction models. The recent advancements in Micro-Electro-Mechanical Sensor (MEMS)/Nano-Electro-Mechanical Sensor (NEMS) technologies and wireless sensor networks combined with efficient energy scavenging paradigms provide opportunities for long-term, continuous, real-time response measurement and health monitoring of transportation infrastructure systems. This paper presents a summary review of some recent studies that have focused on the development of advanced smart sensing and monitoring systems for highway pavement system with potential applications for long-term airport pavement health monitoring. Some examples of these potential applications include: the use of wireless Radio-Frequency Identification (RFID) tags for determining thermal gradients in pavement layers; self-powered MEMS/NEMS multifunction sensor system capable of real-time, remote monitoring of localized strain, temperature and moisture content in airport pavement that will eventually prevent catastrophic failures such as blow-ups on runways during heat waves

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

Road agencies are constantly being placed in difficult situations when making road maintenance and rehabilitation decisions as a result of diminishing road budgets and mounting environmental concerns for any chosen strategies. This has led practitioners to seek out new alternative and innovative ways of monitoring road conditions and planning maintenance routines. This paper considers the use of innovative piezo-floating gate (PFG) sensors and conventional strain gauges to continuously monitor the pavement condition and subsequently trigger maintenance activities. These technologies can help develop optimized maintenance strategies as opposed to traditional ad-hoc approaches, which often lead to poor decisions for road networks. To determine the environmental friendliness of these approaches, a case study was developed wherein a life cycle assessment (LCA) exercise was carried out. Observations from accelerated pavement testing over a period of three months were used to develop optimized maintenance plans. A base case is used as a guide for comparison to the optimized systems to establish the environmental impacts of changing the maintenance workflows with these approaches. On the basis of the results, the proposed methods have shown that they can, in fact, produce environmental benefits when integrated within the pavement management maintenance system

DESIGNING AN INDUCTIVE SENSOR FOR ROAD TRAFFIC MONITORING SYSTEMS AND CONTROL

The purpose of this study is to design an inductive sensor,which detect a vehicle on the road. The main objectives are to design an inductive sensor using an enameled copper wire and interface it to an electronics circuit. The analyses of experiments will mainly the important part of this project. Then, a demonstration will be held to demonstrate the sensing process using a working model. This sensor can change some work from manual to automatically. Examples of situation that can implement this sensor is to control the barrier automatically on the main gates on the roads, to monitor traffic on a narrow curved portion of the road and to count the number of vehicles from a particular point per unit time. At present, there are a lot of sensors available in the market that uses inductive sensor. Many methods can be used in detecting the presence of vehicle and a complete circuit of inductive sensor has also been developed. The result from these methods will assist in the future work of this project

Energy harvesting technologies and devices from vehicular transit and natural sources on roads for a sustainable transport: state-of-the-art analysis and commercial solutions

The roads we travel daily are exposed to several energy sources (mechanical load, solar radiation, heat, air movement, etc.), which can be exploited to make common systems and apparatus for roadways (i.e., lighting, video surveillance, and traffic monitoring systems) energetically autonomous. For decades, research groups have developed many technologies able to scavenge energy from the said sources related to roadways: electromagnetism, piezoelectric and triboelectric harvesters for the cars’ stress and vibrations, photovoltaic modules for sunlight, thermoelectric solutions and pyroelectric materials for heat and wind turbines optimized for low-speed winds, such as the ones produced by moving vehicles. Thus, this paper explores the existing technologies for scavenging energy from sources available on roadways, both natural and related to vehicular transit. At first, to contextualize them within the application scenario, the available energy sources and transduction mechanisms were identified and described, arguing the main requirements that must be considered for developing harvesters applicable on roadways. Afterward, an overview of energy harvesting solutions presented in the scientific literature to recover energy from roadways is introduced, classifying them according to the transduction method (i.e., piezoelectric, triboelectric, electromagnetic, photovoltaic, etc.) and proposed system architecture. Later, a survey of commercial systems available on the market for scavenging energy from roadways is introduced, focusing on their architecture, performance, and installation methods. Lastly, comparative analyses are offered for each device category (i.e., scientific works and commercial products), providing insights to identify the most promising solutions and technologies for developing future self-sustainable smart roads

A rheology model of soft elastomeric capacitor for Weigh-in-motion application

As a result of fast growing industry, there is an increase in traffic congestion and deterioration of transportation inventory. Real-time traffic characterisation could be used to amoliorate the efficiency of our transportation system. Weigh-In-Motion (WIM) systems offer the advantages of vehicle classification, speed measurement, in addition to weight measurement while vehicles are moving. In this thesis, state-of-the-art WIM systems are discussed and limitations of current technologies are identified. A Soft Elastomeric Capacitor (SEC) that works as a large scale surface strain gauge is introduced to address the limitations in existing techniques and investigated for its applicability as a WIM sensor. Though the novel SEC has potential advantages, the relationship axial strain -to-stress needs to be modeled to enable its utilization as a WIM sensor. A Zener model is selected and modified by the addition of a slider to characterize the polymer behavior. An overstress approach is used to study the resultant stress-strain response owing to its simplicity and computational benefits. Since the overstress approach is data-driven, an experimental testing scheme is used to identify the model parameters. The tests comprise three types of applied strain loading: multi step relaxation, simple relaxation and cyclic compression. Specimens with varying stiffness are employed for these tests.. Numerical simulations for the cyclic compression loading are presented to assess the model performance. The model is found to be capable of reproducing the experimental data with an absolute maximum error value of 0.085 MPa for slow loading rate tests and 0.175 MPa for high loading rate tests. Comparative studies are completed to investigate the impact of patch stiffness on the mechanical behavior of the soft elastomeric capacitor patches. It is observed that as stiffness decreases, the nonlinearity in stress-strain response increase

Fiber-optic interferometric sensor for monitoring automobile and rail traffic

This article describes a fiber-optic interferometric sensor and measuring scheme including input-output components for traffic density monitoring. The proposed measuring system is based on the interference in optical fibers. The sensor, based on the Mach-Zehnder interferometer, is constructed to detect vibration and acoustic responses caused by vehicles moving around the sensor. The presented solution is based on the use of single-mode optical fibers (G.652.D and G.653) with wavelength of 1550 nm and laser source with output power of 1 mW. The benefit of this solution lies in electromagnetic interference immunity and simple implementation because the sensor does not need to be installed destructively into the roadway and railroad tracks. The measuring system was tested in real traffic and is characterized by detection success of 99.27% in the case of automotive traffic and 100% in the case of rail traffic.Web of Science2662995298

Accurate vehicle classification including motorcycles using piezoelectric sensors

Thesis (M.S. ECE)--University of Oklahoma, 2012.Includes bibliographical references (leaves 88-90).State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifier devices currently employ inductive loops, piezoelectric sensors, or some combination of both, to aid in the identification of 13 Federal Highway Administration (FHWA) classifications. However, systems using inductive loops have proven unable to accurately classify motorcycles and record pertinent data. Previous investigations undertaken to overcome this problem have focused on classification techniques utilizing inductive loops signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. Most were off-line classification studies with results not directly intended for product development. Vision, infrared, and acoustic classification systems among others have also been explored as possible solutions. This thesis presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally on the roadway to accurately identify motorcycles from among other vehicles, as well as identify vehicles in the remaining 12 FHWA classifications. An algorithm was formulated and deployed in an embedded system for field testing. Both single element and multi-element piezoelectric sensors were investigated for use as part of the vehicle classification system. The piezoelectric sensors and vehicle classification system reported in this thesis were subsequently tested at the University of Oklahoma-Tulsa campus. Various vehicle types traveling at limited vehicle speeds were investigated. The newly developed vehicle classification system demonstrated results that met expectation for accurately identifying motorcycles