Intelligent Compaction Systems Research Articles

An advanced ICMV for vibratory roller compaction

AbstractThe compaction success of vibratory roller compaction can be assessed by systems for continuous compaction control (CCC) or intelligent compaction (IC) which calculate soil stiffness-proportional quantities based on measurements of the motion behavior of the vibrating drum. However, state-of-the-art intelligent compaction meter values (ICMV) do not only depend on the stiffness of the soil but are also strongly influenced by machine and process parameters. In this paper, the methodology for determining an advanced ICMV is presented, in which the mechanical properties of the soil, the process parameters and geometric relationships in the contact area between the drum and the soil are directly included in the calculation. The methodology is explained on the example of measurement data from a compaction test conducted on sandy gravel with a heavy single-drum roller. The results of the novel ICMV are compared with those of the most widely used IC systems.

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

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.

Automated monitoring and evaluation of highway subgrade compaction quality using artificial neural networks

The compaction quality of a highway subgrade is influenced by the compaction parameters and soil properties. Current practice lacks reliable models to assess this multivariate situation. This paper describes a monitoring and evaluation method based on intelligent compaction (IC) and artificial neural networks (ANNs) to assess the compaction quality of a highway subgrade. Field compaction tests were conducted at various roller speeds and vibratory modes. The compactness was predicted using a particle-swarm-optimization optimized backpropagation neural network (PSO-BP-NN) model. The model was integrated into the IC system to automatically evaluate compaction quality. It also demonstrated the advantage of high prediction accuracy with multiple input variables of operation parameters and soil gradation. By efficiently controlling subgrade compaction, the model can be employed for engineering practice and facilitate IC applications.

A sensing-information-statistics integrated model to predict asphalt material density with intelligent compaction system

Intelligent compaction (IC) is an innovative technology that has been used in road and earthwork construction. However, the current IC technology is unable to measure material density directly as the acceptance criteria by owner agencies. To tackle this issue, the authors have developed a sensing-information-statistics integrated model to predict asphalt material density for 100% coverage of construction area. Instrumented with the satellite navigation system, accelerometer, and infrared sensors, IC rollers measure mechanical responses of roller drums and material temperature in real time. With these measurements, panel data models—including both the multivariate linear and nonlinear models—were developed to predict asphalt material density. A reasoning model was proposed to estimate idiosyncratic errors due to uncertainty of measurements. An information management software was developed to analyze IC measurements with univariate statistics and geo-statistical models. Statistical models were implemented and validated with data collected from four paving projects in the US. Results indicate that the multivariate nonlinear panel data model can predict asphalt material density at the project level for 100% coverage of the construction zone within reasonable accuracy. Therefore, this model may serve as an enhanced quality control and acceptance tool for asphalt pavement construction to improve consistency and uniformity and long-term performances.

Adaptive quality control and acceptance of pavement material density for intelligent road construction

During conventional road construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not be representative of the compacted area and may consist of potential bias. This research presented an innovative material-machine-information and human-decision integrated system for adaptive QC/QA using the intelligent compaction (IC) technology to overcome the above limitation of conventional testing. In this integrated system, compaction properties such as material stiffness are monitored in real time with 100% coverage of compacted areas by instrumented IC rollers. By monitoring compaction process and roller–ground interactions, this system can be used to determine compaction target values, which are in-turn fed back to the IC system to optimize compaction efforts and improve construction quality. This approach can also be used to determine compaction uniformity for the as-built in order to assess pavement performance. Recommended further development of the intelligent road construction technology is presented to fully leverage the IC technologies in the future.

Discussion of “Viscoelastic-Plastic Model of Asphalt-Roller Interaction” by Fares Beainy, Sesh Commuri, Musharaf Zaman, and Imran Syed

Discussion of “Viscoelastic-Plastic Model of Asphalt-Roller Interaction” by Fares Beainy, Sesh Commuri, Musharaf Zaman, and Imran Syed

Research on the Key Technology of Road Machinery Intelligent Compaction

The degree of compaction is a measure of the main evaluation index of road machinery compaction quality, but also affects the working characteristics of vibratory roller. In order to make the vibratory compaction always be in the best working state, we must adjust the operating parameters of the roller based on the real-time compaction pressure degree of the compacted material,for the best compaction effect. The paper analyzes the vibration frequency, amplitude, speed and other main working parameters and the relationship between degree of compaction,discusses how to select the main parameters, and carries out the research of Road Machinery Intelligent compaction system.