Application of Intelligent Compaction Technology for Estimation of an Effective Modulus for a Multilayered Asphalt Pavement

Singh, Dharamveer; Beainy, Fares; Commuri, Sesh; Zaman, Musharraf

doi:10.1061/9780784478493.007

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…Although it is difficult to fuse the two different models because of the limited understanding of ICMVs, some research efforts have been made, e.g. the predictive density ρ ODMS (Minchin et al , 2008) based on the AI statistical model and the estimated modulus M NN (Singh et al , 2015) based on the AI frequency identification. In the future, the level-five ICMVs would be an important topic for IC researchers.…”

Section: Bibliometric Statisticsmentioning

confidence: 99%

“…The abbreviation of the artificial intelligence compaction systems is AI compaction systems. Such as ICA (Singh et al , 2015) and HEMS (Yao et al , 2018), could significantly improve the automation level of construction management by introducing neural network and autonomous driving techniques. However, most of these systems are still under development.…”

Section: Bibliometric Statisticsmentioning

confidence: 99%

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Intelligent compaction practice and development: a bibliometric analysis

Liu

Wang

Chen

et al. 2019

ECAM

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Purpose The purpose of this paper is to provide insights into the current practice, challenges and future development trends of intelligent compaction (IC) technology from a bibliometric perspective. Design/methodology/approach A bibliometric analysis on IC-relevant studies is presented. Through this quantitative manner, insights into the current IC research practice and development trends have been derived from the perspectives of publications and citations, spatial distribution, knowledge construction, structural variations, existing problems, and conclusions and recommendations. Findings Currently, IC applications are confronted with the issues of intelligent compaction measurement values (ICMVs) applicability, autonomous control, specifications and applications. To address the issues, three potential research directions are identified: a comprehensive ICMV measurement system that is designated for single layer analysis; autonomous control mechanisms with integrated management capabilities that can efficiently collaborate all stakeholders; and a standardized application workflow and the cost-benefit evaluation of IC in the context of the full life cycle. Research limitations/implications The literature used in this paper is collected from the Web of Science. Although the database covers almost all the important publications in IC field, studies not indexed by the database are not considered. Originality/value This research quantitatively analyzes the current IC practice and development trends from the perspectives of bibliometric analysis. It provides an overview of the knowledge construction and development of IC technology. The discussions about the problems and the suggested solutions can be useful for those interested in this field.

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Section: Bibliometric Statisticsmentioning

confidence: 99%

Section: Bibliometric Statisticsmentioning

confidence: 99%

Intelligent compaction practice and development: a bibliometric analysis

Liu

Wang

Chen

et al. 2019

ECAM

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“…The quality inspection process of asphalt pavement compaction is usually performed by extracting cores from the finished pavement. However, such solution is destructive in nature and results in having potholes in the pavement [11]. Several contributions [11]- [15] estimated the compaction of asphalt pavement by studying the vibratory roller's response as it compacts the asphalt mat.…”

Section: Introductionmentioning

confidence: 99%

“…However, such solution is destructive in nature and results in having potholes in the pavement [11]. Several contributions [11]- [15] estimated the compaction of asphalt pavement by studying the vibratory roller's response as it compacts the asphalt mat. As the pavement compaction level increases, the mat becomes stiffer, thus the frequency response of the vibratory roller is altered.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks

et al. 2022

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The process of identifying the physical properties of raw construction materials is vital in several industrial and quality assurance applications. Ideally, this process needs to be performed without damaging the sample and at low-cost, while obtaining high-accurate results. In this work, a novel non-destructive construction materials classification tool is proposed. The proposed method is based on passing Wi-Fi signals through the observed samples, then analyzing the Channel State Information (CSI) magnitude and phase components. The collected CSI data packets are pre-processed by performing an averaging operation. Then, the resulting data are divided into training and validation sets and used to train Convolutional Neural Networks (CNNs). Here, the trained CNN models are formulated either as classifiers or regression models, depending on the material under test. If the objective is to sort materials within specific classes, then the CNN is formulated as a classifier. Alternatively, if the goal is to estimate a continuously varying parameter in a material, then the CNN is formulated as a regression model. Furthermore, as per the collected data, the proposed method is used to identify the construction materials based on their thickness, water content value (moisture), and compaction. The obtained experimental results effectively demonstrate the potential and merits of the proposed method. Overall, the trained CNN models achieved a 100% validation accuracy and a low validation loss, which confirms that the method is valid and highly accurate.INDEX TERMS Channel state information (CSI), classification, construction materials, convolutional neural network (CNN), Wi-Fi sensing.[1], [2]. In this work, we focus on observing and measuring three physical parameters namely, thickness, moisture content, and degree of compaction, in different individual raw construction materials. The conducted experiments were designed to study the internal physical properties of the observed materials, as well as evaluate the proposed method's performance robustness to variations in physical traits such as thickness. These parameters have a substantial significance in the industry and quality assurance processes. Furthermore, the existing material classification methods that investigate the mentioned parameters often have a compromise between accuracy, cost, and deployability in practical setups. In the industry, there is considerable interest in measuring the

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“…In summary, the compaction state of pavement materials can be obtained by analyzing the time-frequency characteristics of responsive vibration signals in real time [34]. Based on the signal analysis and operation adjustment, the construction quality could be controlled timely [35,36]. erefore, it is important to research the continuous compaction detection method for the nondestructive monitoring and feedback control [37].…”

Section: Introductionmentioning

confidence: 99%

An Improved Low-Cost Continuous Compaction Detection Method for the Construction of Asphalt Pavement

Jia

Qian

et al. 2019

Advances in Civil Engineering

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To realize the continuous compaction control (CCC) of asphalt pavement during construction, continuous detection method was investigated for the compaction degree values. For the trajectory of rollers, a collaborative positioning method was proposed. For the monitoring of rolling process, an embedded-based detection system was designed. For the evaluation of rolling effect, harmonic analysis was introduced and a new index, vibration compaction energy value (VCVe), was proposed. Positioning experiments were conducted, and the accuracy was improved to 0.48 m. Rolling tests were performed, and typical compaction meter values (CMVs), compaction control values (CCVs), and VCVe were obtained. The referenced compaction degree by conventional way was 94.6%, which was used to calibrate the detected values of compaction degree indexes. The results showed that continuous compaction detection can be achieved based on positioning system and vibration analysis. Compared with CMV and CCV, VCVe is less discrete, more stable, and consistent to describe the compaction state. Though, all the CMV, CCV, and VCVe indexes are unable to be used for quality assurance directly or alone, they could be an aid for quality control. Continuous compaction detection system meets the monitoring requirements of pavement construction at a lower cost and could lay a foundation for the intelligent compaction (IC).

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Application of Intelligent Compaction Technology for Estimation of an Effective Modulus for a Multilayered Asphalt Pavement

Cited by 3 publications

References 6 publications

Intelligent compaction practice and development: a bibliometric analysis

Intelligent compaction practice and development: a bibliometric analysis

Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks

An Improved Low-Cost Continuous Compaction Detection Method for the Construction of Asphalt Pavement

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