Application of vehicle-based sensors assessing highway pavement conditions subject to extreme temperature variation

The technique of using vibration sensors to monitor pavement roughness has been expanding in pavement engineering. The primary objective of this study is to implement cost-effective vibration sensors to predict asphalt roughness and identify critical cracking locations. It has been an increasing discussion in industry whether temperature changes due to climate change will have considerable influence on infrastructure resilience and sustainability. The method presented here uses vehicle-based sensors to assess pavement roughness during extreme hot and cold temperatures in Phoenix, AZ. This project consisted of developing vehicle-based accelerometers and taking monthly road surveys for a year. Five sensors were mounted to a vehicle, four on the tires and one inside the car, as well a sixth smartphone sensor inside the car. This project covers collecting data at pavement temperatures from 40ºF - 150ºF. The analysis consists of converting accelerometer data into international roughness index values using Fourier transforms and using statistical analysis to verify a relationship between pavement temperature and accelerometer vibration. The results show that hot asphalt concrete temperatures increase the amount of observable accelerometer vibration from a vehicle. Sensors mounted near the tires showed to be more reliable than sensors inside the vehicle. This project demonstrates that accelerometer sensing technology is a cost-effective way to advance the day-to-day operations in highway pavement maintenance and management

Mobile assistive technologies for the visually impaired

There are around 285 million visually impaired people worldwide, and around 370,000 people are registered as blind or partially sighted in the UK. Ongoing advances in information technology (IT) are increasing the scope for IT-based mobile assistive technologies to facilitate the independence, safety, and improved quality of life of the visually impaired. Research is being directed at making mobile phones and other handheld devices accessible via our haptic (touch) and audio sensory channels. We review research and innovation within the field of mobile assistive technology for the visually impaired and, in so doing, highlight the need for successful collaboration between clinical expertise, computer science, and domain users to realize fully the potential benefits of such technologies. We initially reflect on research that has been conducted to make mobile phones more accessible to people with vision loss. We then discuss innovative assistive applications designed for the visually impaired that are either delivered via mainstream devices and can be used while in motion (e.g., mobile phones) or are embedded within an environment that may be in motion (e.g., public transport) or within which the user may be in motion (e.g., smart homes)

Pavement Performance Evaluation Using Connected Vehicles

Roads deteriorate at different rates from weathering and use. Hence, transportation agencies must assess the ride quality of a facility regularly to determine its maintenance needs. Existing models to characterize ride quality produce the International Roughness Index (IRI), the prevailing summary of roughness. Nearly all state agencies use Inertial Profilers to produce the IRI. Such heavily instrumented vehicles require trained personnel for their operation and data interpretation. Resource constraints prevent the scaling of these existing methods beyond 4% of the network. This dissertation developed an alternative method to characterize ride quality that uses regular passenger vehicles. Smartphones or connected vehicles provide the onboard sensor data needed to enable the new technique. The new method provides a single index summary of ride quality for all paved and unpaved roads. The new index is directly proportional to the IRI. A new transform integrates sensor data streams from connected vehicles to produce a linear energy density representation of roughness. The ensemble average of indices from different speed ranges converges to a repeatable characterization of roughness. The currently used IRI is undefined at speeds other than 80 km/h. This constraint mischaracterizes roughness experienced at other speeds. The newly proposed transform integrates the average roughness indices from all speed ranges to produce a speed-independent characterization of ride quality. This property avoids spatial wavelength bias, which is a critical deficiency of the IRI. The new method leverages the emergence of connected vehicles to provide continuous characterizations of ride quality for the entire roadway network. This dissertation derived precision bounds of deterioration forecasting for models that could utilize the new index. The results demonstrated continuous performance improvements with additional vehicle participation. With practical traversal volumes, the achievable precision of forecast is within a few days. This work also quantified capabilities of the new transform to localize roadway anomalies that could pose travel hazards. The methods included derivations of the best sensor settings to achieve the desired performances. Several case studies validated the findings. These new techniques have the potential to save agencies millions of dollars annually by enabling predictive maintenance practices for all roadways, worldwide.Mountain Plains Consortium (MPC

A Simplified Pavement Condition Assessment and its Integration to a Pavement Management System

abstract: Road networks are valuable assets that deteriorate over time and need to be preserved to an acceptable service level. Pavement management systems and pavement condition assessment have been implemented widely to routinely evaluate the condition of the road network, and to make recommendations for maintenance and rehabilitation in due time and manner. The problem with current practices is that pavement evaluation requires qualified raters to carry out manual pavement condition surveys, which can be labor intensive and time consuming. Advances in computing capabilities, image processing and sensing technologies has permitted the development of vehicles equipped with such technologies to assess pavement condition. The problem with this is that the equipment is costly, and not all agencies can afford to purchase it. Recent researchers have developed smartphone applications to address this data collection problem, but only works in a restricted set up, or calibration is recommended. This dissertation developed a simple method to continually and accurately quantify pavement condition of an entire road network by using technologies already embedded in new cars, smart phones, and by randomly collecting data from a population of road users. The method includes the development of a Ride Quality Index (RQI), and a methodology for analyzing the data from multi-factor uncertainty. It also derived a methodology to use the collected data through smartphone sensing into a pavement management system. The proposed methodology was validated with field studies, and the use of Monte Carlo method to estimate RQI from different longitudinal profiles. The study suggested RQI thresholds for different road settings, and a minimum samples required for the analysis. The implementation of this approach could help agencies to continually monitor the road network condition at a minimal cost, thus saving millions of dollars compared to traditional condition surveys. This approach also has the potential to reliably assess pavement ride quality for very large networks in matter of days.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Development of a smartphone application to measure pavement roughness and to identify surface irregularities

Pavement roughness is an expression of the unevenness or disturbance in a pavement surface that adversely affects the ride quality of a vehicle. Roughness also affects user delay costs, fuel consumption, tire, and maintenance costs. Roughness is predominantly characterized in terms of International Roughness Index (IRI), which is often measured using inertial profilers. Inertial profilers are equipped with sensitive accelerometers, a height measuring laser, a distance measuring instrument, etc., to measure pavement profile. Modern smartphones are equipped with a number of sensors including a three-axis accelerometer, which has been utilized in this project to collect vehicle acceleration data using an android-based smartphone application. Two data analysis schemes have been developed to determine pavement profile from vehicle vertical acceleration data: a double integration and an inverse state space model. Acceleration data was double-integrated numerically to obtain a surrogate estimate of pavement profile based on the calculated vertical position of the vehicle cab. After noting a fairly significant underprediction of IRI for rough pavement sections with the double integration method, due in part to the dampening effects of the vehicle suspension, an inverse state space model was developed. This model enhances the double integration procedure by considering the physics of the mass-spring-damper system of the vehicle sprung mass as part of the back-estimation of road profile from vehicle cab acceleration. In addition, MATLAB and C# scripts were developed to estimate IRI from the pavement profile, using the procedure specified by ASTM. For initial validation, three test sites were selected to collect pavement profile using an inertial profiler along with acceleration collected using the smartphone application. These results demonstrated the potential for smartphone-measure IRI, as good correspondence to the inertial profiler was found for all but the roughest pavement investigated. The state space model was shown to provide significantly better estimates of IRI for rough pavement sections. Good repeatability between measurement replications was also noted, particularly when the space state model was used. For further validation, pavement roughness data was also collected using six smartphones and four vehicles. It was found that both the smartphone model and the vehicle used for data collection will affect the IRI measurement. However, averaged IRI values measured across all smartphones and vehicles were found to be in good agreement with the inertial profiler measured IRI for most of the pavement sections. The final phase of the study involved preliminary work in using the smartphone application for the purpose of pavement feature identification (bumps and potholes). Acceleration data collected using the smartphone application was filtered using an experimentally determined threshold value of 4 m/s2 to identify occurrences of significant localized distresses, and a MATLAB script has been developed to locate those distresses on a digital map. Finally, the smartphone application was used to collect roughness data over about 60 miles of roadway located in Champaign and Piatt County, IL, and measured IRI data has been integrated into a roadway network map using ArcGIS. In the roadway network map, every 0.1-mile pavement section has been highlighted with different colors based on measured roughness. It is hoped that the approach can be used to help reduce the cost of acquiring pavement roughness data for agencies and to reduce user costs for the traveling public by providing more robust feedback regarding route choice and its effect on estimated vehicle maintenance cost and fuel efficiency

Identification of cost-effective pavement management systems strategies a reliable tool to enhance pavement management implementations

Modeling asset deterioration is a key business process within Transportation Asset Management. Road agencies should budget a large amount of public money to reduce the number of accidents and achieve a high level of service of the road system. Managing and preserving those investments is crucial, even more in the actual panorama of limiting funding. Therefore, roadway agencies have to increase their efforts on monitoring pavement networks and implementing data processing tools to promote cost-effective Pavement Management System (PMS) strategies. A comprehensive PMS database, in fact, ensures reliable decisions based on survey data and sets rules and procedures to analyze data systematically. However, the development of adequate pavement deterioration prediction models has proven to be difficult, because of the high variability and uncertainty in data collection and interpretation, and because of the large quantity of data information from a wide variety of sources to be processed. This research proposes a comprehensive methodology to design and implement pavement management strategies at the network level, based on road agency local conditions. Such methodology includes the identification of suitable indexes for the pavement condition assessment, the design of strategies to collect pavement data for the agency maintenance systems, the development of data quality and data cleansing criteria to support data processing and, at last, the implementation spatial location procedures to integrate pavement data involved in the comprehensive PMS. This work develops network-level pavement deterioration models, and reviews road agency preservation policies, to evaluate the effectiveness of maintenance treatment, which is essential for a cost-effective PMS. It is expected that the resulting methodology and the developed applications, product of this research, will constitute a reliable tool to support agencies in their effort to implement their PMS

Technology and Management for Sustainable Buildings and Infrastructures

A total of 30 articles have been published in this special issue, and it consists of 27 research papers, 2 technical notes, and 1 review paper. A total of 104 authors from 9 countries including Korea, Spain, Taiwan, USA, Finland, China, Slovenia, the Netherlands, and Germany participated in writing and submitting very excellent papers that were finally published after the review process had been conducted according to very strict standards. Among the published papers, 13 papers directly addressed words such as sustainable, life cycle assessment (LCA) and CO2, and 17 papers indirectly dealt with energy and CO2 reduction effects. Among the published papers, there are 6 papers dealing with construction technology, but a majority, 24 papers deal with management techniques. The authors of the published papers used various analysis techniques to obtain the suggested solutions for each topic. Listed by key techniques, various techniques such as Analytic Hierarchy Process (AHP), the Taguchi method, machine learning including Artificial Neural Networks (ANNs), Life Cycle Assessment (LCA), regression analysis, Strength–Weakness–Opportunity–Threat (SWOT), system dynamics, simulation and modeling, Building Information Model (BIM) with schedule, and graph and data analysis after experiments and observations are identified

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 2 comprises the second part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field