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• Acoustic emission could diagnose the fracture failure process of asphalt mixtures. • The wavelet coefficient of AE sequence is relevant to the damage evolution process. • The AE energy could stably reveal the fractal characteristic of AE process. • The waveform fractal dimension could identify the critical fracture condition. Dynamical diagnosis of damage evolution is essential to promote the pavement performance of asphalt materials and prolong their service life. As a non-destructive method, acoustic emission (AE) can be used to assess the degree of deformation or cracking of stressed materials by detecting the energy release from damage points. The objective of the study was to explore the continuous damage evolution behavior of asphalt mixtures by evaluating the variation characteristics of AE parameters and waveforms. Firstly, the three-point bending tests and AE tests were simultaneously conducted on asphalt mixture beams, and AE behaviors associated with the failure process were investigated. Secondly, considering the impact of material heterogeneity on AE transmission, wavelet transform was utilized to qualitatively determine the relationship between wavelet coefficient and damage degree of asphalt mixtures. Lastly, the fractal theory was applied to quantitatively explore the scale-invariance of AE process and the AE mutation of damage process. The results show that the accumulation of AE energy and AE count could reflect the dynamical failure process of asphalt mixtures under different loading rates. The effect of sampling frequency could be presented by time-scale characteristics of AE waveforms. The fracture characteristics of asphalt mixtures could be reflected by amplitude and energy distribution of cumulative AE energy and AE waveforms. The fractal result of AE energy is relatively stable in revealing the flexural failure characteristics of asphalt mixtures. The mutational rise of the waveform fractal dimension from the minimum value is related to the critical fracture condition of asphalt mixtures. [ABSTRACT FROM AUTHOR]

Inspired by self-healing attributes, one of the key topics in the field of pavement engineering is changing the way that asphalt materials behave for prolonged service life and environmental savings. Extensive experimental approaches, models and technologies have been developed to characterize the evolution of healing influenced by internal and external factors. The purpose of this study is to explore the local kinetics of asphalt binders that energetically favor autonomous self-healing as well as its mechanism using free energy method. With consideration of driving forces including internal stress and interfacial forces of attraction (or surface energy) responsible for energy redistribution during the rest period, the healing of an asphalt binder was first modeled as a function of thermodynamic parameters. New healing indexes were then defined by the total free energy change for healing (Δ G H) and fast-rate response ratio (R H) of initial healing in duration to the total rest time. Thirdly, a fatigue-healing test was performed on #70 asphalt binder and SBS-modified asphalt binder to examine the potential and reactivity of healing affected by different damage levels. Finally, the effectiveness of proposed thermodynamic healing indexes in describing healing ability was discussed by considering their relations with properties measured by mechanical analysis. Results show that more negative Δ G H indicates a greater tendency to spontaneous healing while less negative Δ G H suggests a poor healing potential as crack propagates to a certain length. In contrast to #70 asphalt binder, SBS-modified asphalt binder exhibits a superior healing potential on a shorter crack length scale. R H gradually increases with the increase of crack length for both #70 asphalt binder and SBS-modified asphalt binder, implying limited mobility of molecules. By contrast with mechanistic healing indexes assessing properties before and after healing (i.e. the degree of healing), the thermodynamic healing indexes provide a new vision regarding healing potential, which reveals the true ability of a material to autonomously repair damaged areas. • Evolution of autonomous self-healing is characterized using free energy method. • Healing potential Δ G H is established and formulated for a damaged asphalt binder. • The initial healing characteristics R H are quantified at different damage levels. • Thermodynamic healing indexes could reveal the true healing ability of a material. [ABSTRACT FROM AUTHOR]

• Acoustic emission technique is used to monitor the fatigue process of asphalt mixtures. • The evolutionary feature of fatigue damage could be characterized by AE energy and RA-AF. • Different damage behaviors are identified by time and frequency characteristics of AE events. • A new AE index is proposed for assessing the damage state. Fatigue cracking of asphalt materials directly affects the performance and safety of asphalt pavements. It is necessary to identify the materials condition with acoustic emission (AE) as the real time monitoring technique. The objective of this study was to diagnose the damage behavior of asphalt mixtures from the time and frequency characteristics of AE signals. Firstly, the semi-circular bending (SCB) fatigue test and AE detection were conducted to explore the AE response of asphalt mixtures subjected to cyclic loads. Then, the Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) were applied to discuss the dominant frequency characteristics and time–frequency distribution of AE waveforms. Finally, an index (R BD), defined as the ratio of fractal dimension of AE waveforms decomposed by the Discrete Wavelet Transform (DWT), was proposed to evaluate the damage state of asphalt mixtures. The results show that AE energy rate exhibits distinct variation signatures at different damage stages. The average frequency (AF) and rise angle (RA) of AE signals reveal that the coupling effect of tensile and shear cracks promotes the deterioration of asphalt mixtures. Most of the dominant frequencies of AE events generated from cracks are concentrated within the range of 13 to 30 kHz. The discrepancies in the time–frequency distribution of AE events could effectively discriminate the types of damage sources. The proposed index R BD is sensitive to the change of damage state of asphalt mixtures, producing corresponding reductions. [ABSTRACT FROM AUTHOR]

The fractures in asphalt materials evolving from microcracks directly affect the performance of asphalt pavements. Acoustic Emission (AE) technique plays a crucial role in dynamically monitoring the internal structural changes of stressed materials. This study attempted to characterize the fracture mechanism of asphalt mixtures by identifying the characteristics of recoded AE data. Firstly, the three-point bending tests and AE tests were simultaneously conducted to investigate the damage evolution process of asphalt mixtures through the time characteristics of AE parameters. Secondly, the cusp catastrophe model based on AE energy rate was employed to explore the mutational characteristics of AE process and to evaluate the damage state of asphalt mixtures. Finally, the k-means method was applied to distinguish the patterns of AE signals associating with the specific damage behaviors of asphalt mixtures during the flexural failure process. The results show that abrupt change of AE energy rate and cumulative AE energy could reflect the evolutionary characteristics of damage in asphalt mixtures. The mutational analysis of AE energy rate reveals that the damage state of asphalt mixtures subjected to the continuous external forces would change between instability and stability, which could be served as a complementary approach for further understanding the periodic variation characteristics of damage evolution process based on cumulative AE energy. The k-means method could effectively classify the recorded AE signals of asphalt mixtures in three-point bending tests into three distinctive clusters. The potential mechanisms for the clustered signals corresponding to different damage behaviors of asphalt mixtures could be characterized by the parametric characteristics and the wavelet power spectrum of AE signals. [ABSTRACT FROM AUTHOR]

• Acoustic emission could characterize the fatigue damage behavior of asphalt mixtures. • AE energy and amplitude b-value could reflect the stage features of damage evolution. • The distribution of AE event is related to the microstructure of mix and loading mode. • The Felicity ratio could measure the irreversibility of damage evolution. • The wavelet scalogram of AE waveforms could identify the damage mechanism. Understanding the fatigue damage mechanism of asphalt mixtures is essential to prolong the service life of asphalt pavements. Acoustic emission (AE) technique can effectively detect minor damage and evaluate material mechanical properties. The objective of this paper was to investigate the temporal-spatial variation of AE parameters associated with fatigue damage behavior of asphalt mixtures and characterize the irreversibility of damage evolution utilizing the Kaiser effect. Firstly, the semi-circular bending (SCB) tests with different rest time and AE tests were carried out simultaneously to discuss the AE characteristics of fatigue damage behavior of asphalt mixtures. Secondly, the spatial evolution of AE events was analyzed to correlate with the crack propagation path of asphalt mixtures. Finally, the Felicity ratio (R F) and wavelet scalogram were utilized to reveal the irreversible characteristics of the AE process of asphalt mixtures. The results show that the "blank area" in AE energy temporal, representing the intensity of AE activities in asphalt mixtures during the process of microcracks concentration, gradually disappears with the increase of rest time. The inflection point of the decline of AE amplitude b-value could be considered as a precursor to the rupture of asphalt mixtures. The spatial distribution of AE events from disorder to order reflects the influence of the microstructures of asphalt mixtures and loading mode on crack propagation. The change of R F could be divided into the three stages, corresponding to the deformation and compaction stage where 1 ≤ R F , microcracks initiation and concentration stage where 0.4 ≤ R F < 1, and macrocracks extension stage where R F < 0.4. The Kaiser effect mainly exists at the stage of deformation and compaction of asphalt mixtures. After the occurrence of macrocracks, the AE process would lose irreversible characteristics. The damage mechanisms related to the Kaiser effect and the Felicity effect could be identified by the energy distribution of AE waveforms. [ABSTRACT FROM AUTHOR]

• Acoustic emission could accurately detect damage sources of asphalt mixtures. • The AE signal has an effective propagation range due to signal attenuation. • The proposed denoising process could minimize the noise interference. • The enhanced TDOA method could improve the accuracy of the source localization. Fracture of asphalt materials has been a critical issue that affects the fatigue performance and service life of asphalt pavements. Acoustic emission (AE) technique, as a kind of non-destructive testing (NDT) method, can effectively detect minor damage in various materials. The objective of this paper was to propose a damage source localization method suitable for understanding the fracture behavior of asphalt mixtures by AE detection. Firstly, the pencil-lead-break (PLB) tests were conducted on asphalt mixture beams to explore the propagation characteristics of AE signals, and a reasonable layout scheme of AE sensors was determined for monitoring the AE process of asphalt mixtures. Secondly, the wavelet threshold denoising method was utilized to extract effective information from AE signals associated with the pencil-lead breaks on the surface of asphalt mixture beam by simultaneously considering the wavelet basis functions, decomposition levels and threshold rules. Finally, an effective time difference of arrival (TDOA) estimation method combining noise reduction, wavelet decomposition and cross-correlation processing was established to accurately locate the damage source of asphalt mixtures. The results show that there is less serious attenuation of amplitude and energy of AE parameters and frequency spectrum of AE waveforms within the range of 100 mm in asphalt mixtures. The larger signal-to-noise ratio (SNR) and the smaller root mean square error (RMSE) of denoised AE signals indicate that the threshold denoising method optimized by the Fruit Fly Optimization Algorithm (FOA) is more effective for AE detection of asphalt mixtures. The improved TDOA estimation method could obtain a minimum TDOA value, and the calculated locations of AE events are closer to the actual PLB points. [ABSTRACT FROM AUTHOR]