Monitoring of Concrete Placement and Vibration for Real-Time Quality Control

Kim, Tae Hyun; Skibniewski, Mirosław J.

doi:10.3311/ccc2019-011

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…In addition, sensor data demands a lot of storage space, and processing it for ML algorithm analysis requires powerful computing power and advanced data analysis. (2) Image and Video Data: Cameras or other vision sensors capture image and video data of the compactor and the construction site [107,108]. These data can be used to analyze the physical properties of the soil, the position and trajectory of the compactor, and other relevant information.…”

Section: Data Collectionmentioning

confidence: 99%

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Wang,

Li,

Zhang

et al. 2024

Sensors

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Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential.

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Section: Data Collectionmentioning

confidence: 99%

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Wang,

Li,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Automated monitoring and warning solution for concrete placement and vibration workmanship quality issues

Lee

Skibniewski

2022

AI Civ. Eng.

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Placing and vibrating concrete are vital activities that affect its quality. The current monitoring method relies on visual and time-consuming feedbacks by project managers, which can be subjective. With this method, poor workmanship cannot be detected well on the spot; rather, the concrete is inspected and repaired after it becomes hardened. To address the problems of retroactive quality control measures and to achieve real-time quality assurance of concrete operations, this paper presents a monitoring and warning solution for concrete placement and vibration workmanship quality. Specifically, the solution allows for collecting and compiling real-time sensor data related to the workmanship quality and can send alerts to project managers when related parameters are out of the required ranges. This study consists of four steps: (1) identifying key operational factors (KOFs) which determine acceptable workmanship of concrete work; (2) reviewing and selecting an appropriate positioning technology for collecting the data of KOFs; (3) designing and programming modules for a solution that can interpret the positioning data and send alerts to project managers when poor workmanship is suspected; and (4) testing the solution at a certain construction site for validation by comparing the positioning and warning data with a video record. The test results show that the monitoring performance of concrete placement is accurate and reliable. Follow-up studies will focus on developing a communication channel between the proposed solution and concrete workers, so that feedbacks can be directly delivered to them.

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Vision method based on deep learning for detecting concrete vibration quality

Ren

Wang

et al. 2023

Case Studies in Construction Materials

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Monitoring of Concrete Placement and Vibration for Real-Time Quality Control

Cited by 4 publications

References 16 publications

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Automated monitoring and warning solution for concrete placement and vibration workmanship quality issues

Vision method based on deep learning for detecting concrete vibration quality

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