Author: Publisher:
ISBN:
Category : CD-ROMs
Languages : en
Pages : 119
Book Description
Pavement Evaluation Using Integrated Data from High-speed Sensors
Author: In-situ Evaluation of Asphalt Pavement Modulus with Embedded Wireless Sensors
Author: Cheng ZhangPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The in-situ dynamic modulus properties of asphalt mixture play a significant role in assessing pavement mechanical responses under traffic loading, determining the pavement performance and condition, and making optimized maintenance decisions. Several methods, such as the falling weight deflectometer (FWD), have been utilized as a non-destructive test to back-calculate the in-situ pavement modulus and conditions; however, the FWD test can only be performed periodically and has the disadvantage of disturbing traffic due to lane-closure needs. With the recent advancement in data science and sensing technologies, the application of micro-electromechanical system (MEMS) sensors and machine learning techniques in pavement nondestructive tests has attracted more research attention. This research aims to develop an in-situ evaluation system that can automatically collect, process, and interpret data to determine the in-situ dynamic modulus of the asphalt mixture under traffic loads using embedded wireless sensors and machine learning techniques. The proposed system is a self-adaptive process and can predict in-situ dynamic modulus based only on mechanical responses and environmental conditions. Ultimately, the well-trained predictive model can be integrated into the pavement management system for the automated and cost-effective assessment of pavement conditions, facilitating informed decision-making. The research program encompasses three types of dynamic modulus experiments: laboratory uniaxial dynamic modulus tests, the one-third scale model mobile load simulator (MMLS3) tests, and in-situ dynamic modulus tests. Particle-size wireless sensors, SmartKli sensors, were implemented in the laboratory specimens and the pavements to collect data from sine wave loads and moving loads. Finite element models (FEM) were also developed and calibrated to generate pavement mechanical response data for more pavement types. The collected data and the FEM simulations were integrated into a database for a proposed adaptive data processing procedure. In addition, because the data collected by embedded sensors in infrastructure health monitoring are inevitably contaminated with noise, and the data features have a distinct discrepancy in different types of tests, a secondary objective of this research is to propose a data processing method capable of removing noises, recognizing data feature discrepancies, and extracting hidden features. An adaptive data processing procedure was developed by combining an empirical mode decomposition (EMD) method and an intrinsic mode function (IMF) selection processing to enhance the reliability of the pavement dynamic modulus prediction. Different EMD techniques were applied to decompose signals from wireless sensors embedded in the pavements. The maximum normalized cross-correlation (MNCC) and signal noise ratio (SNR) were selected as indices in the K-means classification to select the effective IMFs. The results indicated that ensemble EMD (EEMD) and multivariant EMD (MEMD) methods can extract more information from the mechanical responses and extend data dimensions. The EEMD method gives the lowest mean relative error (MRE). Therefore, the EEMD method was recommended for infrastructure data processing. The K-means method can adaptively select the effective IMFs based on the MNCC and SNR. Finally, three dynamic modulus predictive models were developed for different situations. An artificial neural network (ANN) model was developed based on the laboratory test data. This model verified that the ANN model can predict in-situ dynamic modulus. The second dynamic modulus predictive model was developed using the ensemble ANN model to improve the stability of the ANN model, which was trained and tested by the data collected from the MMLS3 test. The third model was developed to predict the dynamic modulus of various asphalt mixtures by fusing a transfer learning approach and Transformer architecture. Besides, the training database was extended with the FEM simulations. The results indicated that the ensemble ANN model is feasible and robust in predicting the dynamic modulus of the asphalt mixture in the MMLS3 test. The transfer learning model is reasonable and robust in predicting the in-situ dynamic modulus of the asphalt pavement.
Data Analysis in Pavement Engineering
Author: Qiao DongPublisher: Elsevier
ISBN: 0443159297
Category : Technology & Engineering
Languages : en
Pages : 378
Book Description
Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems. Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis. This book is the first comprehensive resource to cover all potential scenarios of data analysis in pavement and transportation infrastructure research, including areas such as materials testing, performance modeling, distress detection, and pavement evaluation. It provides coverage of significance tests, design of experiments, data mining, data modeling, and supervised and unsupervised machine learning techniques. It summarizes the latest research in data analysis within pavement engineering, encompassing over 300 research papers. It delves into the fundamental concepts, elements, and parameters of data analysis, empowering pavement engineers to undertake tasks typically reserved for statisticians and data scientists. The book presents 21 step-by-step case studies, showcasing the application of the data analysis method to address various problems in pavement engineering and draw meaningful conclusions.
An Integrated Pavement Data Management and Feedback System (PAMS)
Author: S. C. ShahNondestructive Testing of Pavements and Backcalculation of Moduli
Author: Albert Jasper BushPublisher: ASTM International
ISBN: 0803112602
Category : Kaldırımlar-Deneme
Languages : en
Pages : 701
Book Description
Scientific and Technical Aerospace Reports
Author: Accelerated Pavement Testing to Transport Infrastructure Innovation
Author: Armelle ChabotPublisher: Springer Nature
ISBN: 3030552365
Category : Technology & Engineering
Languages : en
Pages : 724
Book Description
This volume gathers the latest advances, innovations, and applications in the field of accelerated pavement testing (APT), presented at the 6th International Conference on Accelerated Pavement Testing, in Nantes, France, in April 2022. Discussing APT, which involves rapid testing of full-scale pavement constructions for structural deterioration, the book covers topics such as APT facilities, APT of asphalt concrete and sustainable/innovative materials, APT for airfield pavements, testing of maintenance and rehabilitation solutions, testing of smart and multi-functional pavements, data analysis and modeling, monitoring and non-destructive testing, and efficient means of calibrating/developing pavement design methods. Featuring peer-reviewed contributions by leading international researchers and engineers, the book is a timely and highly relevant resource for materials scientists and engineers interested in determining the performance of pavement structures during their service life (10+ years) in a few weeks or months.
Evaluation and Validation of a High-speed Multi-function System for Automated Pavement Condition Survey
Author: Manjriker GunaratnePublisher:
ISBN:
Category : Flatness measurement
Languages : en
Pages : 282
Book Description
Advancing Innovative High-speed Remote-sensing Highway Infrastructure Assessment Using Emerging Technologies
Author: Paul John CarlsonPublisher:
ISBN:
Category : Bridges
Languages : en
Pages : 366
Book Description
Asset management is a strategic approach to the optimal allocation of resources for the management, operation, maintenance, and preservation of transportation infrastructure. Asset management combines engineering and economic principles with sound business practices to support decision making at the strategic, network, and project levels. One of the key aspects of the development of asset management is data collection. The way in which transportation agencies collect, store, and analyze data has evolved along with advances in technology, such as mobile computing (e.g., laptops, tablets), sensing (e.g., laser and digital cameras), and spatial technologies (e.g., global positioning systems [GPS], geographic information systems [GIS], and spatially enabled database management systems). These technologies have enhanced the data collection and integration procedures necessary to support the comprehensive analyses and evaluation processes needed for asset management. Data collection is costly. In determining what data to collect, agencies must weigh these costs against the potential benefits from better data. Traditional pavement and bridge management approaches are data intensive, requiring extensive data collection activities of most or all pavement and bridge assets on an annual or biannual basis. These efforts can be justified given the cost of agencies’ pavement and bridge programs. However, depending on the level of technology needed and the associated costs, it may be difficult to justify similarly extensive data collection efforts for safety and operation assets. While many of the technology innovations and improved data collection processes have been in the bridges and pavements area, there are emerging technologies in the safety and operations infrastructure areas that have yet to be applied to the transportation space. These technologies are driving the costs and efficiencies to the point that makes good sense in terms of the tradeoffs between fiscal responsibilities and advantages of having the data. Therefore, while this research covers all highway infrastructure areas, it includes an emphasis on technologies to assess safety and operations infrastructure. Ultimately, through the three-phased approach, the research strives to bundle the best technologies that maximize sensors and computing power in an effort to achieve the vision of one day having an all-in-one data collection system for infrastructure assessment.
Development of an Integrated Survey Vehicle for Measuring Pavement Surface Conditions at Highway Speeds: Technical report
Author: James C. WamboldPublisher:
ISBN:
Category : Pavements
Languages : en
Pages : 176
Book Description
The objective of this study was to develop an integrated survey vehicle for measuring pavement surface conditions at highway speeds. This was accomplished by determining the requirements and operating characteristics for such a system, preparing a design, and estimating initial and operating costs. Volume 1 contains a review of the data and measurement needs for pavement condition surveys. The equipment available and under development for this purpose are identified with emphasis on measurements at highway speeds. Volume 2 contains detailed information about the design, costs, specifications, and software associated with the integrated survey vehicle. The system drawings for the vehicle are provided.