Intelligent Compaction Measurement Research Articles

Experimental study on the influence of subgrade moisture content on compaction quality control indexes

ABSTRACT Intelligent compaction (IC) technology, which is based on the relationships between the subgrade stiffness properties and intelligent compaction measurement values (ICMVs), has been widely used in the compaction quality control (QC) of subgrade construction. The influence of moisture content (MC) on the stiffness properties is significant and well known but is often ignored in the current practice of IC. In this study, the relationship between the vibration compaction value (VCV) and subgrade reaction modulus (K 30) considering the influence of moisture content is investigated. The results show that the influence of MC on quality control indexes gradually becomes significant, especially in the later stage of compaction. Furthermore, K 30 is more sensitive than the VCV to moisture content, which means that the influence of MC is easy to ignore when taking the VCV as the ICMV. Finally, the specified VCV should be obtained when the MC is on both sides of the optimum moisture content (OMC) to ensure the accuracy of the QC of compaction.

Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality

Intelligent compaction (IC) is an innovative and modified technology used for quality control in subgrade digital construction. However, current intelligent compaction measurement values (ICMVs) cannot provide accurate measurement of the filling layer when the strength of the underlying layer is relatively high. Experimental field tests conducted in the cut to fill subgrade were performed to collect and analyze the variability of ICMV called the vibration modulus (Evib). Furthermore, a new ICMV called the modulus of vibration compaction (Evc) that could remove the interference of the underlying layer and reveal the actual compaction state of filling layer is presented based on the theoretical analysis and numerical simulation method. It was also extracted to study the variability of the filling layer’s compaction state. The results of the above research indicate the influence of the underlying layer’s stiffness on overall compaction quality is remarkable. It was found that it is more likely to achieve the variability control of the compaction state of the filling layer by a new ICMV called Evc. The measured data, improved approaches, and associated conclusions that are presented provide valuable information for researchers and employees considering the use of IC technology.

Research on intelligent compaction stiffness index and its application based on three-degree-of-freedom elastic–plastic dynamical model

CMV (Compaction Meter Value) and stiffness coefficient ks are the two most commonly used indexes in the intelligent compaction technology for subgrade soil. However, the applicability of CMV is affected by the operation mode and the working parameters of the vibrating roller. The calculation model of stiffness coefficient ks does not consider the plastic characteristics of the soil and the data usage rate of its calculation method is low. As a result, the current ICMVs (intelligent compaction measurement values) are only applicable to the compaction quality characterization in the later stage of compaction, and the accuracy and applicability of the compaction quality characterization in the whole process of construction are low, which limits the application of intelligent compaction technology. In view of the above problems, this paper aims to establish a comprehensive stiffness coefficient index which is not affected by the operation mode of the roller, comprehensively reflects the elastic–plastic mechanical properties and has strong resistance to accidental factors, in order to provide an accurate basis for the characterization of subgrade soil compaction quality. Firstly, a numerical simulation model of subgrade soil vibrating compaction with real-time change of soil Mohr-Coulomb model parameters with triaxial strain response is established, which provides accurate data support for ICMV calculation. Secondly, the calculation model and application method of CMV and ks are analyzed. Aiming at the shortcomings of the two indexes, the comprehensive stiffness coefficient kcs reflecting the elastic–plastic characteristics of soil and its complete calculation method are established. Finally, based on the simulation data, the accuracy of kcs is verified, and the variation law of the kcs calculation model parameters and the evolution mechanism of soil elastic–plastic properties are clarified. Based on the field tests data, the accuracy of kcs is verified. The results show that the deciding coefficients between kcs and compaction degree obtained by numerical simulation and field test are R2 = 0.83 and R2 = 0.72, respectively, which are higher than that between ks and compaction degree (R2 = 0.72, R2 = 0.52) and that between CMV and compaction degree (R2 = 0.04, R2 = 0.23). The accuracy of kcs to characterize compaction quality is higher, and it can maintain high stability during field application. During the compaction process, the elastic–plastic mechanical properties of subgrade soil evolve continuously, and each parameter of kcs calculation model increases with the rolling pass.

An Intelligent Compaction Analyzer: A Versatile Platform for Real-Time Recording, Monitoring, and Analyzing of Road Material Compaction.

Intelligent compaction (IC) is a technology that uses non-contact sensors to monitor and record the compaction level of geomaterials in real-time during road construction. However, current IC devices have several limitations: (i) they are unable to visualize or compare multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; (ii) they are not retrofittable to different conventional rollers that exist in the field; (iii) they do not incorporate corrections for ICMVs reflecting variable field conditions; (iv) they are unable to integrate construction specifications as needed for performance-based compaction; and (v) they do not record all the key roller parameters for further compaction analysis. To address these issues, an innovative retrofittable platform with cutting-edge hardware and software was developed. This platform, called the intelligent compaction analyzer (ICA) platform, is effective at calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters and at displaying the spatial distributions of these parameters in a color-coded map in real-time during compaction.

A new approach to maximising the benefits of current intelligent compaction technology for asphalt materials

This paper investigates the correlations between asphalt density, intelligent compaction measurement values (ICMVs) and asphalt moduli obtained from different laboratory experiments at various temperatures of the asphalt mix AC14 with C320 bitumen. It was found that the influence of the density of asphalt on its modulus is primarily governed by the temperature of the asphalt mix. To examine the influence of asphalt temperature on ICMV-density correlations, a vibrating hammer instrumented with an accelerometer was employed to compact cylindrical asphalt specimens at different temperatures. The correlations between ICMVs and asphalt density at different temperatures showed that ICMVs are insensitive to the change in asphalt density at typical compaction temperatures. A practical method to maximise the benefits of the current intelligent compaction (IC) technology for quality control (QC)/quality assurance (QA) of asphalt pavements is proposed in this study. The proposed approach involves overlaying the ICMV map produced during the pre-mapping of the existing layer and the surface temperature map produced during the final roller pass of the newly-laid asphalt layer compaction to identify the most vulnerable regions for random spot density tests. The effectiveness of this approach was demonstrated for an asphalt overlay testbed compacted using an instrumented vibratory roller.

Study of Regression Algorithms and Influent Factors between Intelligent Compaction Measurement Values and In-Situ Measurement Values

Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus ELWD) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and Evib, were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and ELWD, were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction.

In-situ spot test measurements and ICMVs for asphalt pavement: lack of correlations and the effect of underlying support

ABSTRACT The main aim of this paper is to analyse the performance of different intelligent compaction measurement values (ICMVs) and compare them with spot test measurements for asphalt pavement. To accomplish this, a two-layer asphalt testbed was constructed using an instrumented double-drum roller. All ICMVs [compaction meter value ( C M V ), compaction control value ( C C V ), roller-integrated stiffness ( k b ), and vibratory modulus ( E V I B )] were calculated using the same accelerometer data collected during the construction of the asphalt testbed. The analysis of ICMV data showed a strong correlation between k b and E V I B , while C M V and C C V demonstrated a strong correlation only when double jump of the roller drum did not occur. Further, none of the ICMVs recorded during the last roller pass of asphalt compaction showed an acceptable correlation with asphalt densities [measured using a non-nuclear density gauge (NNDG)], mainly due to the influences of the underlying support and asphalt temperature on ICMVs. A correction method to decouple the influence of underlying support on E V I B was developed and validated using existing data from the literature. The applicability of this method for different field scenarios was demonstrated using numerical modelling.

Compaction Prediction for Asphalt Mixtures Using Wireless Sensor and Machine Learning Algorithms

Compaction is a critical step in asphalt roadway construction to determine the pavement’s quality and service life. Current field compactions are mainly based on test strips and engineers’ experiences. Such empirical-based approaches sometimes cause compaction problems especially when new materials are implemented. Intelligent compaction (IC) technology was invented to improve compaction quality. The real-time collection and visualization of the pavement responses and temperature can greatly improve compaction uniformity. However, the material viscoelastic property and the complex pavement structure still hinder the establishment of the correlation between the Intelligent Compaction Measurement Value (ICMV) and the compactability of asphalt pavement. This paper aims to establish compaction prediction models based on machine learning (ML) algorithms and sensing technology. Given the strong correlation between the mixture’s compactability and the particle compaction characteristics, a particle-size wireless sensor, SmartRock, was used to collect the particle kinematic behaviors during compaction. 11 asphalt mixtures were compacted with an embedded SmartRock sensor. Three ML models and three dataset scenarios were applied to predict the compaction condition, and a higher than 98% prediction accuracy was achieved. The results revealed that combining the ML approach and sensing data is appropriate and practical for predicting the compaction condition of the asphalt mixture. Future studies shall further evaluate the algorithm based on field compaction data so that intelligent sensing technology can be implemented to improve compaction quality.

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 Resistivity Plate Loading Device for Assessing the Factors Affecting the Stiffness of a Cement-Stabilized Subgrade.

The extent of mixing in the stabilization process and the control of the cement content (C) and water content (w) in the mixture are key to the outcome of the engineering performance of a cement-stabilized subgrade. Intelligent Compaction (IC) quality control has improved quality control and management practices during construction. Intelligent Compaction Measurement Values (ICMVs) selected to evaluate the stiffness properties of cement-stabilized soils do not directly relate to the stiffness properties of the cement-stabilized subgrade and do not consider w and C. Additional tests need to be conducted for calibration of ICMVs. In this study, our solution is the development of a resistivity plate loading test. The resistivity plate loading test features the flexibility in determining the soil stiffness, w, C, and other important factors, such as the time of test effect (hydration) (T) and dry density (ρd). To verify the accuracy of the testing method, laboratory experimental studies were conducted on cemented soils considering ρd, w, C, and T at different factor levels. Multiple response studies based on grey rational analysis (GRA) were conducted. Analysis of the input factors was performed, and their effects on the measured responses were quantified. According to the study, the ρ measured by the device was a powerful indicator of stiffness, ρd, w, C, and T, which showed that the device can be useful equipment for quality control and an advancement in the in situ testing technologies and test equipment. A statistical regression model based on the linear and linear plus interaction terms among the factors is proposed to predict the average responses.

A state-of-the-art review of compaction control test methods and intelligent compaction technology for asphalt pavements

This paper investigates the current state of knowledge of the existing compaction testing methods for asphalt and identifies the limitations of these methods in using them during asphalt pavement compaction. Conventional spot tests that are carried out at limited spots for quality control (QC) and quality assurance (QA) of asphalt compaction often fail to ensure the uniformity of compaction. The differential approach using microwave sensors attached to the rollers can qualitatively indicate the optimum density during asphalt compaction; however, it requires spot density measurements to quantify the asphalt density achieved. intelligent compaction (IC) can be used to ensure the uniformity of asphalt compaction and to get real-time feedback. The current IC specifications for soil compaction are not suitable for asphalt compaction due to the viscoelastic nature of asphalt and the variation of asphalt mat temperature during compaction in the field. In addition, the state-of-the-art intelligent compaction measurement value (ICMVs) recorded during asphalt compaction does not correlate well with the asphalt density while it shows a reasonable correlation with asphalt stiffness. The effects of asphalt mat temperature and underlying support on ICMVs measured by IC rollers are identified as the potential causes of the poor correlation between ICMVs and spot density measurements. It is proposed that the relationship between asphalt modulus used in pavement designs and the ICMVs corrected for the effects of asphalt mat temperature and underlying support needs to be investigated in order to establish performance-based specifications for QA/QC of asphalt compaction. The emerging research on using GPR for asphalt density estimation is examined and the factors that affect the GPR measurements during asphalt pavement compaction are identified .

Improving asphalt pavement intelligent compaction based on differentiated compaction curves

Although the Intelligent Compaction (IC) technology has been widely used on the asphalt pavement compaction nowadays, the potential of compaction curve and the target IC measurement value (ICMV) has still not been well clarified, and the actual practices using IC rollers mainly focus on the roller passes and asphalt temperature. The present study investigated the interrelations among asphalt temperature, compactibility, dynamic modulus, and ICMV through resurfacing projects in Tennessee. It was found that the original IC compaction curve could only offer limited information, and the target ICMV value was strongly affected by the asphalt temperature. A further investigation using asphalt vibratory compactor, gyratory compactor, and Witczak model indicated that the effect of temperature on the asphalt modulus and its compactibility varies. To explore the potential of IC technology, the original IC compaction curves were further divided according to the initial compaction temperature, and the practical “cessation temperature” for the breakdown roller could be identified. Based on the findings, the criteria to improve the IC resurfacing compaction were suggested, and it was well in accordance with the test results of core samples. As a result, the asphalt temperature and roller passes are still suggested for the IC asphalt compaction. However, it is based on a better understanding of IC by the differentiated compaction curves.

Incorporating Calibrated Numerical Models in Estimating Moduli of Compacted Geomaterials from Integrated Intelligent Compaction Measurements and Laboratory Testing

Proof rolling after completion of compaction of earthwork using intelligent compaction (IC) technology can be used to assess the uniformity of the compacted geomaterials and to identify less stiff compacted areas. To better understand the process of quality management using IC technology, a three-dimensional finite element model was developed to document the theoretical limitations and the sensitivity of IC technology. The model can also be used for setting the target values, and can be part of a methodology to extract the moduli of geomaterials in more rigorous and defensible specifications. Different levels of complexity in relation to linear versus nonlinear behavior of geomaterial and different levels of roller operating conditions (static versus dynamic, moving versus stationary) can be assigned to the model. Such model can only be used with confidence if it is calibrated and validated with experimental field data. Five test sites including single-layer (subgrade only) and two-layer (subgrade and base) geosystems were instrumented using in-ground sensors for calibrating the model. Different vibratory IC rollers from different manufacturers were used to implement IC tests at vibratory stationary as well as moving conditions at the test sites. In all scenarios, a locally calibrated transfer function was necessary to accommodate the differences in the numerical models and vibration data obtained from the IC measurements on test sections with distinct geomaterial conditions. The study also concluded that the predictive power of the numerical models improves if the field measurements and laboratory data are integrated, and more importantly the variability of the materials is incoprorated.

Influence of moisture content on intelligent soil compaction

The intelligent compaction (IC) technology has been demonstrated as a powerful tool for soil compaction. However, the correlation between IC measurement values (ICMV) and in-situ tests has not been always consistent due to the influence of moisture content. The same density can be obtained at two different moisture contents on either side of the optimum moisture content, whereas without the same modulus. In this study, the influence of moisture contents on ICMV and in-situ point measurement values were examined by both in-situ and laboratory tests. By using the compaction curves from the gyratory compactor and field compaction, the impact of different moisture contents on soil modulus was identified, and the sensitivity of ICMV on the soil density and deflection due to varying moisture contents was explained. Another laboratory test, the vibratory compaction, could offer the target ICMV value from a modulus versus water content curve. Based on the test results in this study, an improved IC soil compaction system including laboratory test validations was suggested to identify the soil compactability and the target value at varying moisture contents.

Intelligent compaction practice and development: a bibliometric analysis

PurposeThe 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/approachA 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.FindingsCurrently, 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/implicationsThe 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/valueThis 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.

Spatial Verification of Modulus for Pavement Foundation System

This paper presents the results of a field study recently completed on an Illinois Tollway construction project, in which intelligent compaction (IC) measurements were calibrated on site with in situ elastic modulus (E), resilient modulus (Mr), modulus of subgrade reaction ( k), and California bearing ratio (CBR) testing. The purpose of the field calibration was to generate geo-referenced spatial modulus maps that can be used for field verification of design input parameters. E values were obtained using light weight deflectometer (LWD), stress-dependent Mr values and static k values were obtained using automated plate load testing (APLT), and CBR values were obtained using a dynamic cone penetrometer (DCP). The IC measurements were obtained using a vibratory smooth drum roller outfitted with an index measurement value system, and a roller outfitted with a retrofit kit programmed to produce validated integrated compaction (VIC) measurements through a process of field calibration. Results showed that VIC based calibration can produce correlations with R2 > 0.9 with Mr and k values, and the VIC maps can be reliably used for quality assurance/verification testing. Although the index based IC measurement values showed statistically significant relationships, the R2 values were lower (<0.6). An implementation framework that emphasizes an independent certification approach for field calibration is being developed based on these test results for the Illinois Tollway, to aid in the effective implementation of the modulus-based mapping approach for pavement design field verification.

Geostatistical analysis of intelligent compaction measurements for asphalt pavement compaction

Abstract Although unable to address the issue of spatial uniformity, univariate statistics are commonly used to evaluate the compaction uniformity of asphalt layers nowadays. Intelligent compaction (IC) technology can provide the spatial IC measurement values (ICMV) with 100% coverage during compaction and offers an opportunity to perform the geostatistical analysis on the compacted asphalt layers. In this study, the construction quality of two typical asphalt pavement projects, including one new pavement construction project and one resurfacing construction project, were evaluated by performing geostatistical analysis for ICMV. Some critical issues regarding the use of geostatistical method for the evaluation of construction quality were also addressed by the case studies. The results from the geostatistical analyses show that IC technology can offer detailed information about spatial compaction uniformity. Upon comparison of the spatial uniformity between different layers, the semivariogram after the normal score transformation was suggested due to the measuring depths of the IC roller. The spatial statistics of ICMV could be adopted to monitor the changes in spatial uniformity during compaction. The factors affecting ICMV and geostatistical analysis were further discussed.

Impact of Geospatial Classification Method on Interpretation of Intelligent Compaction Data

Intelligent compaction is an emerging technology in the management of pavement layers, more specifically, of unbound geomaterial layers. Different types of intelligent compaction measurement values (ICMVs) are available on the basis of the configuration of the roller, vibration mechanism, and data collection and reduction algorithms. The spatial distribution of the estimated ICMVs is usually displayed as a color-coded map, with the ICMVs categorized into a number of classes with specific color codes. The number of classes, as well as the values of the breaks between classes, significantly affect the perception of compaction quality during the quality management process. In this study, three sets of ICMV data collected as a part of a field investigation were subjected to geostatistical analyses to evaluate different classification scenarios and their impact on the interpretation of the data. The classification techniques were evaluated on the basis of the information theory concept of minimizing the information loss ratio. The effect of the ICMV distribution on the selection of the classification method was also studied. An optimization technique was developed to find the optimal class breaks that minimize the information loss ratio. The optimization algorithm returned the best results, followed by the natural breaks and quantile methods, which are suited to the skewness of the ICMV distribution. The identification of less-stiff areas by using the methods presented will assist highway agencies to improve process control approaches and further evaluate construction quality criteria. Although the concepts discussed can apply to any compacted geomaterial layer, the conclusions apply to the type of compacted soil in this particular test section.

Quality control of subgrade soil using intelligent compaction

AbstractIntelligent Compaction (IC) of subgrade soil has been proposed to continuously monitor the stiffness of subgrade during its compaction. Modern IC rollers are vibratory compactors equipped with (1) an onboard measuring system capable of estimating the stiffness of the pavement material being compacted, (2) Global Positioning System (GPS) sensor to precisely locate the roller, and (3) an integrated mapping and reporting system. Using IC, the roller operator is able to evaluate the entire subgrade and address deficiencies encountered during compaction. Continuous monitoring of quality during construction can help build better quality and long-lasting pavements. However, most of the commercially available IC rollers report stiffness in terms of Original Equipment Manufacturer (OEM) specified indicator, known as Intelligent Compaction Measurement Value (ICMV). Although useful, additional tests are required to establish the correlation between these ICMV values and the resilient modulus of subgrade (Mr). Since the mechanistic design of the pavement is performed usingMr, it is important to know if the designMris achieved on the entire subgrade during compaction. This paper presents a systematic procedure for monitoring the level of compaction of subgrade in real time using intelligent compaction (IC). Specifically, the Intelligent Compaction Analyzer (ICA) developed at the University of Oklahoma was used for estimating the modulus of the subgrade. Results from two demonstration studies show that the ICA is able to estimate subgrade modulus with an accuracy that is acceptable for quality control activities during the construction of pavements.

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.