A prototype knowledge-based system for pavement analysis

Highway engineers have addressed the problem of pavement maintenance by developing remaining life assessment methods based on structural analysis of computer simulations of pavements tested in the field by non-destructive testing devices such as the Falling Weight Deflectometer (FWD). However the methodologies followed have been shown to be unable to provide accurate solutions without undue reliance on the knowledge of the expert engineer who conducts the analysis. A knowledge-based system (KBS) is proposed to "inject" engineering knowledge into the conventional techniques. It has been established on a systematic basis and seeks to cover the variety of the issues which may be encountered in such systems. In its prototype form the system consists of three parts: 1. The finite element analytical program ROSTRA-1. 2. A deductive database. 3. A back-analysis subsystem. The analytical program carries out the analysis of the pavements tested in the field. The deductive database holds the properties of a variety of paving materials and establishes the analytical model. The back-analysis subsystem seeks to perform the tasks required for the analysis of the FWD deflection bowl. To build this system, the POPLOG-Prolog computer language operated under VAX/VMS was selected to work in connection with the analytical program. An evaluation procedure was carried out to investigate the performance characteristics of the prototype system. The results indicated that the POPLOG-Prolog development environment is not the ideal tool for such an application. In addition, it appears unlikely that there is any other development tool available which is markedly more effective than that used. However it is felt that similar functions to those required by the POPLOG-Prolog environment, may be implemented using conventional programming. To permit this, a logical design of a KBS to conduct this task is presented

A Comparative Study of Insertion Loss of Traffic Noise Barriers in Georgia

In this paper, three types of traffic noise barriers, interlocking steel panels, precast concrete panels, and Paragon panel 23-T, were evaluated in terms of insertion loss. Field test was conducted for the noise barriers recently installed as part of the Northwest Express Project in Georgia. The noise insertion loss was measured as noise difference in A-weighted decibels (dBA) immediately before and after the barriers. The insertion loss was then evaluated by correlating with noise barrier types and other influential variables, including the separation distance of barriers from traffic, the level of traffic, wind speed, and pavement types. The results showed that under prevailing conditions represented by other influential variables, all three barrier types achieved an insertion loss in the range of 7.02 dBA to 13.58 dBA, exceeding the noise reduction design goal of 7 dBA as stated in the Georgia Department of Transportation’s noise abatement policy. Among the three, the Paragon panel 23-T barriers effected the highest insertion loss, followed by the precast concrete panel barriers and the interlocking steel panel barriers

A Framework to Incorporate a Structural Capacity Indicator into the State of Louisiana Pavement Management System

Non-structural factors such as surface distresses and ride quality have been commonly used as the main indicators of in-service pavement conditions. In the last decade, the concept of implementing a structural condition index in Pavement Management System (PMS) to complement functional condition indices has become an important goal for many highway agencies. The Rolling Wheel Deflectometer (RWD) provides the ability to measure pavement deflection while operating at the posted speed limits causing no user delays. The objective of this study was two-fold. First, this study developed a model to predict pavement structural capacity at a length interval of 0.16 km (0.1 mi.) based on RWD measurements and assessed its effectiveness in identifying structurally deficient pavement sections. Second, this study introduced a framework, along with the required implementation tools, for incorporating pavement structural conditions into the Louisiana PMS decision matrix at the network level. The proposed framework aims at filling the gap between network level and project level decisions and eventually, allowing more accurate budget estimation. To achieve these objectives, RWD data collected from 153 road sections (more than 1,600 km) in District 05 of Louisiana were utilized in this study. The predicted Structural Number (SNRWD0.1) showed an acceptable accuracy with a Root Mean Square Error (RMSE) of 0.8 and coefficient of determination (R2) of 0.80 in the validation stage. Core samples showed that sections that were predicted to be structurally-deficient suffered from asphalt stripping and material deterioration distresses. Results support that the developed model is a valuable tool that could be used in PMS at the network level to predict pavement structural condition with an acceptable level of accuracy. With respect to the implementation of RWD in Louisiana PMS, two enhanced decision trees, for collectors and arterials, were developed, such that both functional and structural pavement conditions are considered in the decision-making process. Implementation of RWD in the decision-making process is demonstrated and is expected to improve the overall performance of the pavement network. Furthermore, the enhanced decision trees are expected to reduce the total maintenance and rehabilitation (M&R) construction costs if applied to relatively high volume roads (e.g., Interstates, Arterials, and Major Collectors). Based on the results of this study, a one-step enhanced decision-making tool, which considers both structural and functional pavement conditions in treatment selection, was developed. In the developed tool, the predicted structural number based on RWD measurements was utilized to calculate a pavement structural health indicator known as the Structural Condition Index (SCI). Finally, an Artificial Neural Network (ANN)-based pattern recognition system was trained and validated using pavement condition data and RWD measurements-based SN to arrive at the most optimum maintenance and rehabilitation (M&R) decisions

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

Pavement Performance Prediction Using Machine Learning and Instrumentation in Smart Pavement

The optimization of pavement Maintenance and Rehabilitation (M&R) planning and costs has been historically proven as a complex task. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) applications in pavement engineering data analytics have been gaining momentum. These advanced techniques have shown promising results in civil engineering and transportation asset management. Therefore, designing a smart pavement framework that relies on the actual pavement responses and in-service condition can help with utilising the ML approach toward better understanding the performance of pavements. To implement the concept of “Smart Pavement”, constructing an interactive pavement pilot section that provides the necessary data feedback to improve the decision-makings of M&R would be needed. This thesis focuses on some aspects of the design of in-situ pavement monitoring and the applying selected machine learning techniques for pavement performance prediction. In order to design an effective pavement instrumentation plan, a literature review was conducted to identify and evaluate the major in-situ monitoring devices and previous case studies. Innovative technologies of Structural Health Monitoring (SHM) were also discussed as a part of the sensory system. A potential pilot section was identified by the Region of Waterloo, for which the pavement structure and technical details were retrieved. Based on the results from the literature review and the evaluation of the proposed section details, a preliminary instrumentation layout has been proposed. Next, the interaction between the proposed embedded sensors and surrounding pavement structure under traffic loading was further studied to evaluate the effect of pavement instrumentation on actual structural responses. Therefore, a series of finite element analysis (FEA) scenarios were defined, and modelling was conducted using ABAQUS to quantify the artefact impacts of the sensors on the pavement responses. Based on the FEA results, high stress- and strain-concentration areas were located which can be used to optimize the design of sensor layout, leading to capturing representative critical pavement responses. Consequently, sensor spacing criteria were suggested to avoid device interference for the response measurement. Furthermore, it would be informative to know how, and which AI/ML methods have been previously used for pavement performance prediction purposes. A systematic literature review iii was conducted indicating that majority of studies used Artificial Neural Network (ANN) of which the prediction process is unexplainable to predict International Roughness Index (IRI) resulting in high prediction accuracies (R2 >= 0.9). A Decision Tree (DT) model and a Random Forest (RF) model were developed using the most commonly used input data retrieved from the Long-Term Pavement Performance (LTPP) database to predict IRI. Finally, after the pruning process, the DT model and RF model resulted in a cross-validation accuracy (R^2) of 0.846 and 0.859, respectively. The single tree from the DT model is less complex than the trees from the RF model. Further studies on machine learning model development should be conducted to refine prediction accuracy. Finally, recommendation for future data collection standards from pilot sections were provided to help with developing a pavement response database that can overcome the inconsistencies in the existing LTPP database and potentially improve the reliability of the future pavement performance modelling

Distress and failure of pavement systems.

Massachusetts Institute of Technology. Dept. of Civil Engineering. Thesis. 1969. Ph.D.MICROFICHE COPY ALSO AVAILABLE IN BARKER ENGINEERING LIBRARY.Two unnumbered leaves inserted. Vita.Bibliography: leaves 189-196.Ph.D

Analyzing Cost Effectiveness of Photovoltaic Pavements

The United States Air Force (USAF) is the largest consumer of energy within the Department of Defense (DoD). As such, the USAF is continually looking for ways to reduce consumption, as well improving network resiliency and assuring supply. One potential method for addressing these items is focusing on applications of renewable energy. A specific application of renewable energy that could greatly benefit the USAF if viable would be photovoltaic (PV) pavements. PV pavements would be able to capitalize upon the large swathes of pavements on Air Force (AF) installations, while not being hampered by other concerns such as clear zones for aircraft. One way to evaluate viability of a technology is through analyzing cost-effectiveness. While initial efforts were not directly focused on cost-effectiveness, the information gathered helped pave the way for such an analysis. Specifically, previous researchers at the Air Force Institute of Technology (AFIT) designed and implemented an experimental system for collecting performance data on horizontally oriented PV panels. Data was collected from 38 sites worldwide for a time period of up to one year. Five installations were then selected from the 38 original sites to utilize in determining cost-effectiveness. As part of evaluating cost-effectiveness, average power generation values were determined from the data. This information, along with pavement construction costs, helped form the basis of developing a model to evaluate life cycle costs for PV pavements. The model was then applied to each installation a total of 60 times to evaluate individual effectiveness. At the worst-case cost of construction for PV pavements, $460/SM, none of the installations evaluated would be able to consider installation PV pavements a viable alternative to traditional asphalt pavements

Development of a Wireless Real-Time Productivity Measurement System for Rapid Bridge Replacement

Increased attention has been paid to rapid bridge replacement, one of the critical components of the nation’s transportation network, since the terrorist attacks on September 11, 2001. To enhance the capability of rapid replacement of damaged bridges after extreme events, a prototype wireless real-time productivity measurement system has been developed. The developed system has a potential not only to improve the accuracy of construction schedule but also to strengthen the communication and coordination among parties involved in the replacement process after extreme events by providing accurate productivity information in real time. To validate the developed system, field experiments were conducted at three construction sites. Results of data analyses indicate that it is feasible to use the developed system to measure on-site productivity in real time; and productivity measurements were accurate and could be shared among all parties involved in the replacement process