Numerical simulation of intelligent compaction for subgrade construction

During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

The pioneer of intelligent construction—An overview of the development of intelligent compaction

As the pioneer in the intelligent construction technologies(ICT)of transportation infrastructure,intelligent compaction(IC)has been applied in the infrastructure construction of various countries.It is currently the technology that best reflects the intelligence of engineering construction.This article overviews the latest developments and trends in IC.Firstly,the basic meaning of ICT is defined based on the essential characteristics of intelligent construction of transportation infrastructure,“perception,analysis,decision-making,execution”(PADE).The concept of intelligent compaction technology classification is also introduced.The PADE requirements that intelligent compaction should meet are proposed.Secondly,according to the sequence of“perception,analysis,decision-making,execution,”the workflow and key technologies of intelligent compaction are analyzed,and the mechanism of using the response of the roller to solve the modulus is given and verified.On this basis,The IC feasibility test methods,including compaction degree,compaction stability,and compaction uniformity,are briefly described.The implementation scheme of feedback control is given.Then,the use of artificial neural networks(ANN),hybrid expert systems,and reinforcement learning in intelligent compaction are briefly introduced.Finally,several extended applications of intelligent compaction are expounded,including the development ideas of intelligent road rollers and the role of intelligent compaction in virtual construction,the layer-specific mechanical parameters of fillers,etc.

Intelligent compaction theory of high roller compacted concrete dam

The concept and realization process of intelligent compaction for the construction of high roller compacted concrete dam were presented, as well as the theory of monitoring and intelligent feedback control. Based on the real-time analysis of the compaction index, a multiple regression model of the dam compactness was established and a realime estimation method of compaction quality for the entire work area of roller compacted concrete dam was proposed finally. The adaptive adjustment of the roiling process parameters was achieved, with the speed, the exciting force, the roller pass and the compaction thickness meeting the standards during the whole construction process. As a result, the compaction quality and construction efficiency can be improved. The research provides a new way for the construction quality control of roller compacted concrete dam.

Field investigation of intelligent compaction for hot mix asphalt resurfacing

Intelligent compaction （IC） is a relatively new technology for asphalt paving industry. The present study evaluated the effectiveness and potential issues of the IC technology for flexible pavement resurfacing construction using two field projects. In the first project, a geostatistical semivariogram model was established and the parameters derived from it were compared with univariate statistical parameters for the Compaction Meter Value （CMV） data. Further analyses illustrated the effect of temperature on the CMV value and compaction uniformity. In the second project, a multivariate analysis was performed between in situ tests and IC data. The possibility of combining various IC data to predict the asphalt layer density and improve the current quality control and assurance system was discussed.