1,097 research outputs found

    Research News, November 2010

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    Newsletter of the Iowa Department of Transportation's Research and Technology Burea

    Contractor-Furnished Compaction Testing: Searching for Correlations Between Potential Alternatives to the Nuclear Density Gauge in Missouri Highway Projects

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    The Missouri Department of Transportation’s (MoDOT) past and present Quality Control and Quality Assurance programs for construction are examined. MoDOT’s present Quality Management program along with a small number of grading projects has lowered the number of Quality Assurance (QA) soil compaction tests completed in the past two years. The Department would like to rid itself of using the Nuclear Density Gauges because of burdensome Federal regulations, required training, security and licensing fees. Linear and multiple regression analysis was performed to see if a correlation between nuclear density gauge dry densities values and Light Weight Deflectometer modulus values/ Clegg Hammer Clegg Impact Values exist. These relationships or lack thereof will determine the technology used by construction contractors to perform compaction quality control testing if MoDOT moves away from using nuclear density gauges for soil density verification

    Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

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    [EN] With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project(No.2020-zlkj-04); National Social Science Fund projects (No.20BJY010); National Social Science Fund Post-financing projects (No.19FJYB017); Sichuan-tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No.2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (No.2018-GH-006 and No.2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No.E-GKRWJC20202914); Shaanxi Social Science Fund (No.2017S004); Xi'an Construction Science and Technology Planning Project (No.SZJJ201915 and No.SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No.19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University (No.300102239616, No.300102281669 and No.300102231641).Ma, Z.; Zhang, J.; Philbin, SP.; Li, H.; Yang, J.; Feng, Y.; Ballesteros-PĂ©rez, P.... (2021). Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement. Buildings. 11(12):1-18. https://doi.org/10.3390/buildings11120577S118111

    Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

    Get PDF
    With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site

    Coupling the Road Construction Process Quality Indicators into Product Quality Indicators

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    Advances in Asphalt Pavement Technologies and Practices

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    Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on “Advances in Asphalt Pavement Technologies and Practices” curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face

    Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

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    The energy or fuel consumption of the millions of vehicles that daily operate in road pavements has a significant economic and environmental impact on the use phase of road infrastructures regarding their life cycle analysis. Therefore, new solutions should be studied to reduce the vehicles energy consumption, namely due to the tire-pavement interaction, and contribute towards the sustainable development. This study aims at estimating the energy consumption due to the rolling resistance of tires moving over pavements with distinct surface characteristics. Thus, different types of asphalt mixtures were used in the surface course to determine the main parameters influencing the energy consumption. A laboratory scale prototype was developed explicitly for this evaluation. Data mining techniques were used to analyze the experimental results due to the complex correlation between the data collected during the tests, providing meaningful results. In particular, the artificial neural network allowed to obtain models with excellent capacity to estimate energy consumption. A sensitive analysis was carried out with a five input parameter model, which showed that the main parameters controlling the energy consumption are the vehicle speed and the mean texture depth.ERDF funds, through the Competitivity Factors Operational Programme – COMPETE, and by national funds, through FCT – Foundation for Science and Technology, within the scope of the Strategic Project UID/ECI/04047/2013 and the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio

    Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

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    Recycling; Machine learning; Artificial neural networksReciclaje; Aprendizaje automático; Redes neuronales artificialesReciclatge; Aprenentatge automàtic; Xarxes neuronals artificialsThis paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.Open access funding provided by Università degli Studi di Palermo within the CRUI-CARE Agreement. Part of this research was funded by the project RTI2018-096224-J-I00 that has been cofounded by the Spanish Ministry of Science and Innovation, inside the National Program for Fostering Excellence in Scientific and Technical Research, National Subprogram of Knowledge Generation, 2018 call, in the framework of the Spanish National Plan for Scientific and Technical Research and Innovation 2017–2020, and by the European Union, through the European Regional Development Fund, with the main objective of Promoting technological development, innovation and quality research. Part of this work was financially supported by the Italian Ministry of University and Research with the research Grant PRIN 2017 USR342 Urban Safety, Sustainability and Resilience
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