Compaction Quality Assessment Research Articles

Developing a real-time compaction quality assessment methodology for subgrade based on semi-supervised co-training

The current intelligent methodologies for real-time evaluation of subgrade compaction quality predominantly rely on supervised machine learning algorithms. However, the scarcity of sand cone test significantly impedes model performance, thereby severely limiting the applicability of intelligent subgrade compaction. This paper proposes a semi-supervised co-training algorithm for real-time evaluation of subgrade compactness during the construction procedure, leveraging unlabeled data to enhance the model performance. Based on the compaction datasets from various subgrade scenarios, the proposed PSO-XGB-Co-training KNN-PLS (PSO-XGB-CoKP) algorithm is utilized to train the unlabeled data, boasting a 20.4% reduction in Mean Squared Error (MSE). The semi-supervised co-training algorithm is modified by employing different regressors as co-trained sub-models and increasing the number of regressors. The model is optimized by levering the accuracy and computational cost, and an optimal data augmentation volume is recommended through the sensitivity study. This study provides an alternative approach for leveraging unbalanced and small-sample datasets to develop a reliable intelligent methodology for evaluating subgrade compaction quality in engineering practice.

Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

The method for calculating phase angle between exciter force of vibration exciter and roller displacement

Introduction. In the process of soil compaction it is important to have information about the current density of the layer, as it enables to quickly adjust the load on the compacted material. The field methods of compaction quality assessment do not cope with this task, as they make point estimation within the pavement area. Therefore, continuous compaction monitoring systems installed on vibratory road rollers are becoming increasingly common. The systems developed by BOMAG and AMMANN require, among other things, the phase angle between the exciter force and the roller movement to calculate the compaction quality index. The phase angle is determined by the unbalance position sensor, which is very labor-intensive. In addition, continuous compaction monitoring systems include an accelerometer. The purpose of this paper is to develop an indirect method for calculating the phase angle from accelerometer readings.The method of research. In order to achieve the purpose of the work, a roller-soil single-mass model in a typical mode for vibratory rollers (periodic loss of contact) has been studied. As a result of modeling it has been found that the reaction of the compacted material has the main influence on the vertical component of a roller acceleration and practically does not affect the horizontal component. This is confirmed by the experimental data.Results. The phase angle can be determined by mutual correlation of the horizontal and vertical acceleration signals of the roller obtained with the accelerometer.Conclusion. The study proposes a new method of calculating the phase angle between the exciter force and the roller displacement, which eliminates the direct measurement of this angle. The calculation of the angle is based on the readings of a two-axis accelerometer installed on the road roller. The proposed method enables to simplify the system of continuous compaction control and reduce the labor intensity of phase angle measurement.

Compaction quality assessment of highway using roller-integrated positive maximum kinetic energy increment detection technique

During conventional highway construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not comprehensively represent the entire compacted area and are vulnerable to potential biases. Based on roller-integrated positive maximum kinetic energy increment detection technique with real-time kinematic global positioning systems, this research adopted kinetic energy compaction value (KECV) as a real-time monitoring index for the highway compaction quality, and subsequently proposed an KECV-based assessment method to estimate the compaction quality of subgrade and pavement materials. Additionally, a global interpolation method (Green spline) was adopted to obtain estimates for both KECVs and compaction quality at any location on the work area, enabling the analysis of the passing rate of compaction quality for the entire work area. A case study on the Hengyong highway project in China indicates a highly linear correlation between the KECV and the compaction parameters, the compactness of LLL silt, and the relative density of cement-stabilized macadam; therefore, it can serve as a reliable index for monitoring compaction quality. Assessment of compaction quality on the entire work area can be fast and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of highways.

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory.

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

Assessment of compaction quality of highway subgrade via roller-integrated positive maximum velocity detection technique

ABSTRACT Quality control of compaction is vital for the long-term performance of highways. Specifically, the assessment of subgrade compaction quality is a key factor. Currently, compaction quality is primarily controlled by parameters set during compaction and subsequent random sampling tests post-construction. However, the impact of human factors and limitations of these sampling tests fall short of meeting the standards of large-scale precision construction. This study introduces a real-time monitoring index to characterise the compaction quality of silt subgrades: the maximum velocity compaction value (MVCV). This index is derived using a combination of the roller-integrated positive maximum velocity detection system and real-time kinematic Beidou positioning system (RTK-BDS). Furthermore, the Green spline interpolation method allows for estimating compaction quality values at any point. A case study from the Hengyong Highway project in China reveals a strong linear relationship between compaction parameters and compactness. This suggests the suitability of this method for characterising the compaction quality of such silt. This assessment approach facilitates a detailed evaluation of compaction quality across the entire construction zone. Hence, it aids in effectively minimising compaction defects, reducing unnecessary rework, and enhancing the overall compaction quality and efficiency of highway construction.

3D conditional random fields simulation for rockfill compaction quality assessment with sparse EVD measurement

The compaction quality of rockfill materials plays a pivotal role in ensuring the stability and durability of runway structures. However, traditional assessment methods relying on limited random sampling may lack representativeness for the entire construction site and overlook potential areas of insufficient compaction. Addressing this challenge, this paper employs the dynamic deformation modulus (EVD), a rapid and nondestructive testing method, to assess the compaction quality of runway rockfill. Subsequently, this paper propose a 3D Conditional Random Field (CRF) model based on EVD to simulate the uncertain spatial distribution of rockfill compaction quality, allowing for the estimation of the overall construction area's compaction quality with a limited number of sample points. The validity of the model is demonstrated through a case study involving a runway expansion project. Additionally, three critical construction factors--namely, measurement points, compaction passes, and compaction materials--are analyzed throughout the construction process. This analysis provides managers with valuable insights and decision-making tools to promptly evaluate the quality of compaction areas, ensuring the stability and durability of the runway.

A LOF-IDW based data cleaning method for quality assessment in intelligent compaction of soils

Intelligent compaction (IC) is attracting increasing interest in construction engineering involving earthwork compaction. However, outliers may exist in data sets collected during IC, which can lead to misinterpretation of the compaction status of soils and may further result in erroneous results for compaction quality assessment. This study proposes a novel method for cleaning the measured data sets by combining the density-based local outlier factor (LOF) and inverse distance weighted (IDW) method. In the proposed LOF-IDW based method, the effect of spatial variation in soil properties is taken into account, while a boxplot-based method is proposed to dynamically determine the suitable threshold for outlier diagnosis. The capability and performance of the proposed method are verified against three data sets collected from construction sites. The results indicate that compared with the commonly used 3σ criterion, the proposed method not only exhibits a better performance in outlier diagnosis but also can rehabilitate the relevant data. In addition, the proposed method is demonstrated to be capable of significantly reducing the coefficient of variation of the measured data sets, which provides a more reliable support for the quality assessment and decision-making in IC.

Investigating real-time monitoring indices of compaction quality from particle movement characteristics of distinctly-graded unbound aggregate materials subjected to vibratory compaction

The compaction quality greatly affects the in-service performance of unbound aggregate materials (UAMs); however, there still lack real-time, non-destructive, and effective indices for compaction quality assessment. This paper aimed to reveal meso-scale mechanisms of macroscopic vibratory compaction behavior and evaluate compaction quality from particle movement characteristics. A series of newly-developed laboratory vibratory plate compaction tests were conducted on UAM specimens of different gradations. The innovative self-powered, wireless sensors resembling realistic particles (termed as “SR sensors”) were placed inside UAM specimens to monitor three-dimensional (3D) particle accelerations and Euler angles during vibratory compaction. The effects of gradation and vibratory frequency on both macroscopic compaction behavior and meso-scale particle motion characteristics were analyzed. The results show that the entire vibratory compaction process can be divided into three distinct stages according to discernibly-staged particle movement patterns. The degree of compaction (DOC) of UAMs is significantly correlated with two novel particle-motion-based compaction quality indicators.

Compaction quality assessment of cement stabilized gravel using intelligent compaction technology—A case study

As a new compaction quality management system of road construction, the intelligent compaction (IC) technology has shown a great potential for quality control and acceptance (QC/QA) in the compaction of cement stabilized gravel (CSG) subbase. However, before it achieves a high level of technology readiness, there are some challenges to be solved. One of the critical problems is that there is no reliable correlation between IC measurement values (ICMVs) and field test measurements, which is thus focused on. The influence of moisture content on the relationships between ICMVs and density were studied. Two types of ICMVs, roller-integrated stiffness (Kb) and compaction meter value (CMV), were collected and their correlations with material compactness were established respectively. In addition, it discusses the changing pattern of compaction degree and its uniformity with roller passes. The results show that, moisture content has a significant effect on the correlation between ICMVs and compaction degree of CSG. Compared with CMV, Kb has a higher regression coefficient with the in-situ measurements. Also, it is proven that the compaction degree and uniformity of CSG do not necessarily increase with the development in the number of rolling passes. Finally, this study makes a comprehensive evaluation of the compaction quality of the test section based on the relationships between ICMVs and in-situ test results.

A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer

Adequate and uniform compaction is essential for the safety and durability of a pavement structure. The current compaction quality control, which relies on limited spot tests data, cannot provide timely feedback about compaction quality. The main objective of this paper is to develop a novel data-driven method for compaction quality assessment of cement-stabilized base in a real-time manner. The basic idea is to consider the drum of the vibratory roller together with the underlying cement-stabilized base layer as a spring-mass-dashpot system. The compaction level of the base is connected to the stiffness of the system and to the corresponding natural frequencies. In this study, the vibration of the drum was monitored during operation. The fundamental mode natural frequency of the system at different compaction levels of the base was identified by the fast Bayesian fast Fourier transform method. The results indicate that the natural frequency of the vibratory system has a positive correlation with the degree of compaction (DOC) of the underlying cement-stabilized base layer. Therefore, a novel and practical method for on-site compaction quality assessment utilizing measured natural frequency is proposed in this paper. A data-driven classification method based on support vector machine (SVM) was developed to realize the compaction quality evaluation. The classification results show that the proposed method can recognize signal patterns corresponding to different DOCs. The proposed method combines vibration theory and machine learning to provide a technical reference for the research of continuous compaction control with vibratory rollers.

Effective compaction power index for real-time compaction quality assessment of coarse-grained geomaterials: Proposal and comparative study

Assessing the compaction quality of geomaterials is important for quality assurance and control during the rolling process of earthworks. However, accurately assessing the compaction quality of coarse-grained geomaterials in real time still remains a challenge. The compaction quality of geomaterials is closely related to compaction energy. Therefore, a new method that uses effective compaction power (ECP) to assess the compaction quality of geomaterials is proposed. We define ECP; model the walking, vibration, and transmission systems of the roller to calculate ECP; and then propose a real-time ECP measurement and integration method. Field experiments showed that the ECP index demonstrated a high correlation with the compaction parameters, the dry density of coarse-grained geomaterials (such as rock-fill and filter materials), and the compactness of core wall materials. The error margin of the ECP index-based compaction quality assessment was small. The proposed method can realize accurate, real-time compaction quality assessments of entire work areas. Thus, this study has broad application prospects in road and dam constructions.

Roller-Integrated Compaction Assessment of Earth-Rock Dam Materials Considering Operation Modes

Abstract An earth-rock dam is composed of multiple materials compacted by rollers with complex operation modes, which can easily lead to the abnormal fluctuation of harmonic-ratio-based roller-integrated compaction measurement values (HR-RICMVs). Without proper handling, the abnormal fluctuation would result in unreliable assessment of compaction quality. As an attempt to address the issue of abnormal fluctuation, this study presented an improved regression model for roller-integrated compaction assessment of earth-rock dam materials. The proposed model considered the effect of vibration frequency on HR-RICMVs under different operation modes. The applicability of the improved model was verified by an analysis of correlation and reliability in different earth-rock dam materials. The results indicate that the model has significant merits in the assessment of compaction quality under complex operation modes. This research provides a potential way for the compaction quality control of earth-rock dam materials based on HR-RICMVs in future studies.

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

AbstractCompaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

Compaction quality assessment of rockfill materials using roller-integrated acoustic wave detection technique

Abstract During conventional earth-rock dam 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. Based on roller-integrated acoustic wave detection technique with real-time kinematic global positioning systems, this research adopted sound compaction value (SCV) as a real-time monitoring index for the dam compaction quality, and subsequently proposed an SCV-based assessment method to estimate the compaction quality of rockfill (RF) materials. Additionally, a geostatistical method (Kriging) was adopted to obtain estimates for both SCVs and compaction quality at any location on the work area, thereby calculating the qualified rate of compaction quality for the entire work area. A case study on the Qianping project in China indicates a strong linear correlation between the SCV and the compaction parameters as well as the dry density of RF materials; therefore, it can serve as a reliable index for monitoring compaction quality of RF materials. Assessment of compaction quality on the entire work area can be quickly and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of earth-rock dams.

Rockfill dam compaction quality evaluation based on cloud-fuzzy model

The quality of compaction is key to the safety of dam construction and operation. However, because of incomplete information about the construction process and the unknown relationship between compaction quality and the factors that influence it, traditional evaluation methods such as neural networks and multivariate linear regression models fail to take uncertainty fully into account. This paper proposes a cloud-fuzzy method for assessing compaction quality by considering randomness, fuzziness, and incomplete information. The compaction parameters and material source parameters are the key parameters in the assessment of compaction quality. A five-layer neural-network model of compaction quality assessment is established that considers compacted dry density and its classification membership and probability as the criteria, and the rolling speed, rolling passes, and compacted layer thickness as alternatives. Because of uncertainties in the criteria and alternatives, the cloud-fuzzy method, in which a fuzzy neural network is extended with a cloud model to handle uncertain and fuzzy problems more effectively, is introduced to determine the compaction quality. A case study is presented to evaluate the compaction quality of a hydropower project in China. The results indicate that the cloud-fuzzy model is feasible in relation to precision and makes up for the sole focus on precision by traditional methods. The proposed method provides a triple index for understanding compaction quality, which facilitates assessment of the compaction quality of an entire dam surface.

Evaluation of Compaction Quality Based on SVR with CFA: Case Study on Compaction Quality of Earth-Rock Dam

AbstractThe compaction quality of earth-rock dam materials is a major concern in the evaluation of earth-rock dams. Current compaction quality assessment methods, such as graphical reports or simpl...

Assessment of Compaction Quality Based on Two Index Parameters from Roller-Integrated Compaction Measurements

Abstract The average degree and uniformity of compaction are two important aspects of continuous compaction control. This paper presents the development and assessment of two new indices for evaluating the average degree and uniformity of field compaction. The proposed index δ accounts for measured compaction values and their corresponding area. The proposed index R relates to the spatial distribution of undercompacted areas. In situ compaction data collected from five subgrade sections are used to assess the applicability of the proposed indices. The results show that the combination of δ and R is capable of comprehensively evaluating field compaction quality. It is found that δ < 1 % and R > 0.5 can be used as the acceptance criteria for average degree of compaction and spatial uniformity of compaction, respectively.

Assessment of compaction quality of multi-layer pavement structure based on intelligent compaction technology

Intelligent compaction (IC) is an emerging and evolving technology for road construction. IC technology brings a lot of benefits as well as some challenges to be solved. This paper presents the framework of a field construction using IC technology and the results of the assessment of the practical use of intelligent compaction. The IC technology was implemented at the soil subgrade, the cement stabilized gravel subbase, and the cement stabilized gravel base course. The changing pattern of Compaction Meter Value (CMV) corresponding to roller passes and moisture contents of compacted materials was studied. It was found that it is more likely to achieve the desired compaction quality under the optimum moisture content situation. The relationships between CMV values and in situ measurements (compactness and deflection) for three layers were analyzed using simple linear regression analysis. The results of above linear regressions performed at three layers indicate the potential influence of underlying layers on upper layers. Then the relationships between CMVs of different layers were firstly studied using simple linear regression and multiple linear regression in which the effects of moisture content and compaction quality indicators were also considered. The study on the effect of moisture content on CMV shows that the closer the moisture content to the optimum moisture content, the more likely to get larger CMV values. The strong and consistent correlations between CMVs of different layers obtained from above regression analysis have further demonstrated the contribution of underlying layers to upper layers, and may be useful in the research of CMV decoupling in the future. Furthermore, the proposed moisture content effect study has provided a way for the above multiple factors involved CMV decoupling research in future studies.

Anomalous Data Detection for Roller-Integrated Compaction Measurement

AbstractThis paper presents a theoretical and experimental analysis of an anomalous data detection treatment for roller-integrated compaction measurement (RICM) data. Anomalous data, which may be discovered during the collection of the RICM data, can significantly influence the evaluation of the compaction quality and misrepresent the real compaction situation of the layer. Two types of anomalous data are investigated, and corresponding methods are presented to identify these types. A bidimensional anomalous data identification method is proposed to distinguish anomalous data in calibration tests, and a neighboring weighted-estimation method is presented to reject anomalous data during the compaction quality assessment. The RICM data from three field construction sites are analyzed to verify the applicability and validity of the proposed methods. The results suggest that the first method renders a more accurate correlation, whereas the second method improves the precision of the compaction evaluation.