Review on Design, Characterization, and Prediction of Performance for Asphalt Materials and Asphalt Pavement Using Multi-Scale Numerical Simulation.

Asphalt pavement, which is mainly made up of the asphalt mixture, exhibits complicated mechanical behaviors under the combined effects of moving vehicle loads and external service environments. Multi-scale numerical simulation can well characterize behaviors of asphalt materials and asphalt pavement, and the essential research progress is systematically summarized from an entire view. This paper reviews extensive research works concerning aspects of the design, characterization, and prediction of performance for asphalt materials and asphalt pavement based on multi-scale numerical simulation. Firstly, full-scale performance modeling on asphalt pavement is discussed from aspects of structural dynamic response, structural and material evaluation, and wheel-pavement interaction. The correlation between asphalt material properties and pavement performance is also analyzed, and so is the hydroplaning phenomenon. Macro- and mesoscale simulations on the mechanical property characterization of the asphalt mixture and its components are then investigated, while virtual proportion design for the asphalt mixture is introduced. Features of two-dimensional and three-dimensional microscale modeling on the asphalt mixture are summarized, followed by molecular dynamics simulation on asphalt binders, aggregates, and their interface, while nanoscale behavior modeling on asphalt binders is presented. Finally, aspects that need more attention concerning this study's topic are discussed, and several suggestions for future investigations are also presented.

Experimental Testing and Constitutive Modelling of Pavement Materials.

Pavement materials such as asphalt mixtures, granular aggregates and soils exhibit complex material properties and engineering performance under external loading and environmental conditions [...].

Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.

Weighing-In-Motion (WIM) technology is one of the main tools for pavement management. It can accurately describe the traffic situation on the road and minimize overload problems. WIM sensors are the core elements of the WIM system. The excellent basic performance of WIMs sensor and its ability to maintain a stable output under different temperature environments are critical to the entire process of WIM. In this study, a WIM sensor was developed, which adopted a PZT-5H piezoelectric ceramic and integrated a temperature probe into the sensor. The designed WIM sensor has the advantages of having a small size, simple structure, high sensitivity, and low cost. A sine loading test was designed to test the basic performance of the piezoelectric sensor by using amplitude scanning and frequency scanning. The test results indicated that the piezoelectric sensor exhibits a clear linear relationship between input load and output voltage under constant environmental temperature. The linear correlation coefficient R2 of the fitting line is up to 0.999, and the sensitivity is 4.04858 mV/N at a loading frequency of 2 Hz at room temperature. The sensor has good frequency-independent characteristics. However, the temperature has a significant impact on it. Therefore, the output performance of the piezoelectric ceramic sensor is stabilized under different temperature conditions by using a multivariate nonlinear fitting algorithm for temperature compensation. The fitting result R2 is 0.9686, the root mean square error (RMSE) is 0.2497, and temperature correction was achieved. This study has significant implications for the application of piezoelectric ceramic sensors in road WIM systems.

Review on Load Transfer Mechanisms of Asphalt Mixture Meso-Structure.

Asphalt mixture is a skeleton filling system consisting of aggregate and asphalt binder. Its performance is directly affected by the internal load transfer mechanism of the skeleton filling system. It is significant to understand the load transfer mechanisms for asphalt mixture design and performance evaluation. The objective of this paper is to review the research progress of the asphalt mixture load transfer mechanism. Firstly, this paper summarizes the test methods used to investigate the load transfer mechanism of asphalt mixtures. Then, an overview of the characterization of load transfer mechanism from three aspects was provided. Next, the indicators capturing contact characteristics, contact force characteristics, and force chain characteristics were compared. Finally, the load transfer mechanism of asphalt mixtures under different loading conditions was discussed. Some recommendations and conclusions in terms of load transfer mechanism characterization and evaluation were given. The related work can provide valuable references for the study of the load transfer mechanism of asphalt mixtures.

Performance Testing of Micro-Electromechanical Acceleration Sensors for Pavement Vibration Monitoring.

Pavement vibration monitoring under vehicle loads can be used to acquire traffic information and assess the health of pavement structures, which contributes to smart road construction. However, the effectiveness of monitoring is closely related to sensor performance. In order to select the suitable acceleration sensor for pavement vibration monitoring, a printed circuit board (PCB) with three MEMS (micro-electromechanical) accelerometer chips (VS1002, MS9001, and ADXL355) is developed in this paper, and the circuit design and software development of the PCB are completed. The experimental design and comparative testing of the sensing performance of the three MEMS accelerometer chips, in terms of sensitivity, linearity, noise, resolution, frequency response, and temperature drift, were conducted. The results show that the dynamic and static calibration methods of the sensitivity test had similar results. The influence of gravitational acceleration should be considered when selecting the range of the accelerometer to avoid the phenomenon of over-range. The VS1002 has the highest sensitivity and resolution under 3.3 V standard voltage supply, as well as the best overall performance. The ADXL355 is virtually temperature-independent in the temperature range from -20 °C to 60 °C, while the voltage reference values output by the VS1002 and MS9001 vary linearly with temperature. This research contributes to the development of acceleration sensors with high precision and long life for pavement vibration monitoring.

Reinforcement Effect of Different Fibers on Asphalt Mastic.

Fiber materials as an asphalt mixture additive and stabilizer can effectively improve the performance index of asphalt pavement. In this study, lignin and carbon fiber were used as modifiers to study their effects on the road performance of asphalt mastic. Based on the frequency sweep, linear amplitude sweep (LAS) and multi-stress creep recovery (MSCR) experiments were conducted to test the high-temperature rutting and medium-temperature fatigue resistance of asphalt mastic with different fiber incorporation and low-temperature performance tests based on bending beam rheometer (BBR). The results indicate that adding fibers increased the stiffness of the asphalt mastic, and the modification effect of lignin fibers was better than that of carbon fibers. Meanwhile, the characteristic flow index of the asphalt mastic gradually increased with the increase in temperature, indicating that it gradually became a near-Newtonian fluid at higher temperatures. The addition of fibers also improved the high temperature rutting resistance of the asphalt mastic but did not have an advantageous effect on fatigue and low temperature cracking resistance. Additionally, the fitting results of the four-parameter Burgers model show that the use of fiber modification decreases the proportion of elasticity and viscous creep compliance but increases the delayed elasticity part.

Rheological Behaviors and Damage Mechanism of Asphalt Binder under the Erosion of Dynamic Pore Water Pressure Environment.

Asphalt binder plays an important role in the overall resistance of asphalt mixture to the moisture damage induced by a dynamic pore water pressure environment. This study evaluates the moisture sensitivity of asphalt binder from the perspective of rheological behaviors using the dynamic shear rheometer (DSR) and the bending beam rheometer (BBR) methods at high, medium, and low temperatures. The damage mechanism is further discussed quantitatively based on the Fourier transform infrared spectroscopy (FTIR) method. The results indicate that a longer conditioning duration is beneficial for asphalt binder to recover its adhesion at 60 °C in multiple stress creep recover (MSCR) tests, but the increasing pore water pressure magnitude of 60 psi held an opposite effect in this study. The asphalt binder's fatigue life at 20 °C in linear amplitude sweep (LAS) tests decreased obviously with conditioning duration and environmental severity, but the reducing rate gradually slowed down, while the groups of 50 psi-4000 cycles and 60 psi-4000 cycles held a comparable erosion effect. Both the stiffness and relaxation moduli at -12 °C in the BBR tests exhibited an obvious decreasing trend with conditioning duration and environmental severity. The erosion effect on the asphalt binder was gradually enhanced, but it also exhibited a slightly more viscous performance. Water conditioning induced several obvious characteristic peaks in the FTIR absorbance spectra of the asphalt binder. The functional group indexes presented a trend of non-monotonic change with conditioning duration and environmental severity, which made the asphalt binder show complicated rheological behaviors, such as non-monotonic variations in performance and the abnormal improving effect induced by dynamic pore water pressure conditioning.

Characterization of Rutting Damage by the Change of Air-Void Characteristics in the Asphalt Mixture Based on Two-Dimensional Image Analysis.

In the process of the rutting test, the air-void characteristics in asphalt mixture specimens are a dynamic change process. It is of great significance to systematically study the correlation between the change of air-void characteristics and the depth of the rutting slab and establish a relationship with damage. In this paper, the air-void information of rutting specimen sections with different loading cycles (500, 1000, 1500, 2000, 2500, and 3000 times) is obtained by two-dimensional image technology. The dynamic change process of the micro characteristics of internal air voids of two graded asphalt mixtures (AC-13 and AC-16) under cyclic wheel load is analyzed, and it is used as an index to characterize the microstructure damage of the asphalt mixture. The results show that the variation of air-void distribution, air-void shape characteristics, and air-void fractal dimension with the loading process can well characterize the permanent deformation law of the rutting slab. The fractal dimension of the air void increases with the increase in load. It is a dynamic process in which the air-void content changes with crack initiation and propagation. After rutting deformation, the total air-void area and average air-void size of the sample increase, and the total air-void number decreases. Because microcracks are formed in the specimen after rutting damage, the aspect ratio of the air void increases, and the roundness value decreases.

Deep learning for neural decoding in motor cortex.

Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.

Effects of Different Natural Factors on Rheological Properties of SBS Modified Asphalt.

Typical climatic environments such as UV radiation, high temperature and strong wind in cold and arid regions have a significant effect on asphalt aging. The intent of this work is to reveal the evolution law of natural aging of SBS-modified asphalt under the complex adverse climate environment in cold and arid regions. Furthermore, the contribution rate of various environmental factors of natural aging of asphalt in cold and arid regions was analyzed. Based on rheological parameters, this paper characterized the influence of natural aging on the viscoelastic properties, rutting resistance at a high temperature, fatigue resistance and cracking resistance at a low temperature of SBS-modified asphalt. The evolution law of natural aging performance of SBS-modified asphalt was revealed. A quantitative evaluation index (CIi) of natural aging contribution rate of asphalt was put forward and the contribution rate of various environmental factors to asphalt natural aging was analyzed. The results showed that the effects of simulated aging and natural aging on asphalt properties were similar. After aging, the viscoelastic properties of asphalt were deteriorated, and the risk of fatigue cracking and low temperature cracking was increased. It also enhanced the deformation resistance of asphalt and increased the rutting resistance at high temperature. The aging contribution index CIi obtained based on rheological parameters such as complex modulus and rutting factor could directly reflect the influence of different natural factors on the performance of asphalt during aging. Among them, the effect of thermal oxygen was more obvious on the natural aging of SBS-modified asphalt.

Dosage Effect of Wet-Process Tuff Silt Powder as an Alternative Material of Sand on the Performance of Reactive Powder Concrete.

A large amount of stone powder is produced during the production of machine-made sand. This research aims to study the effect of wet-process tuff silt powder (WTSP) dosages (as an alternative sand material to utilize waste stone powder and reduce environmental hazards) on reactive powder concrete's (RPC) mechanical performance. The physical and chemical properties of WTSP were analyzed as per relevant standards. This study prepared RPC samples with various WTSP content (0%, 6%, 12%, and 18%) to replace quartz sand at the same water-binder ratio (0.14) and allowed the samples to cure for 3 days, 7 days and 28 days prior to unconfined compression testing and flexural testing. Scanning electron microscopy (SEM) and Mercury Intrusion Porosimetry (MIP) testing were also carried out to observe the evolution of macroscopic properties in response to replacing part of quartz sand with the same amount of WTSP. The results show that the developed flexural and unconfined compressive strength (UCS) decreases slowly with a greater dosage of WTSP. However, when the WTSP content is 12% or less, the RPC made with WTSP satisfies the industrial application threshold regarding mechanical properties. For RPC samples containing more than 12% WTSP, the UCS and flexural strength showed a dramatic drop. Thus 12% of WTSP content was deemed the maximum and the corresponding UCS of 104.6 MPa and flexural strength of 12 MPa for 28 days of curing were the optimums. The microscopic characteristics indicate that the addition of WTSP can effectively fill the large pores in the RPC micro-structure, hence reducing the porosity of RPC. Furthermore, the WTSP can react with the cementitious material to form calcium aluminate during the hydration process, further strengthening the interface. The alkaline calcium carbonate in WTSP could improve the interfacial adhesion and make the structure stronger.

Development and application of a field knowledge graph and search engine for pavement engineering.

Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.

The Effect of Cross-Linking Type on EPDM Elastomer Dynamics and Mechanical Properties: A Molecular Dynamics Simulation Study.

The cross-linking structure of the Ethylene-propylene-diene monomer (EPDM) is made up of a number of cross-linking types, including carbon atoms from the main chain or monomer and ether crosslinks formed during degradation. Through molecular dynamic simulations, the contribution of each type of cross-linked structure to the dynamics and mechanical properties of EPDM, the study's focus, were investigated. Cross-linking between the tertiary carbons of two main chains, cross-linking at the monomer's unsaturated position, ether cross-linking after oxidation, and other combinations of target cross-linked carbon atoms from different positions, totaling eight types of cross-linked types, were mixed with EPDM free chains in a 1:1 ratio to form eight types of cross-linked EPDMs. These varieties of cross-linked EPDMs were then compared to an uncross-linked EPDM in terms of density, radius of gyration, free volume, mean square displacement, and uniaxial tensile stress-strain curves. It was found that the cross-linking was always proven to have a favorable influence on mechanical characteristics; however, the relaxation inhibition effect varied. The cross-linking between the diene monomer at the C9 position resulted in a more flexible molecular shape and was more than double the free volume of the uncross-linked EPDM, resulting in an improved diffusion ability. The ether cross-linking produced by the oxidation of the side chain cross-linking improved the positive contribution to stiffness and enhanced the inhibitory impact on diffusion properties, whereas the main chain cross-linking had the opposite effect. The research presented in this study leads to a better knowledge of the microscopic aspects underlying EPDM performance.

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10-2/°C to 1.896 × 10-4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation.

Rollpave pavement, as a rollable prefabricated asphalt pavement technology, can effectively reduce the overall road closure time required for pavement construction and maintenance. Sensors can be integrated into Rollpave pavement, thereby avoiding sensor damage that may otherwise result from high temperatures and compactive forces during the rolling process, as well as pavement structural damage resulting from cutting and drilling. However, the embedment of sensors into Rollpave pavement still presents certain challenges, namely poor interfacial synergy between the embedded sensor and the asphalt mixture. To solve this problem, three-point bending tests and dynamic response FEM simulations were used to optimize the embedded sensor's packaging. The influence of sensor embedment on Rollpave pavement under different working conditions was analyzed. Results of these analyses show that low temperature and the epoxy resin negatively affect the bending performance of specimens, and that packaging with cylindrical shape, flat design, and consisting of a material with modulus similar to that of the asphalt mixture should be preferred. This study is conducive to improve the intellectual level and service life of road infrastructure.

FARCI: Fast and Robust Connectome Inference.

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

Influence of Temperature on the Natural Vibration Characteristics of Simply Supported Reinforced Concrete Beam.

Natural vibration characteristics serve as one of the crucial references for bridge monitoring. However, temperature-induced changes in the natural vibration characteristics of bridge structures may exceed the impact of structural damage, thus causing some interference in damage identification. This study analyzed the influence of temperature on the natural vibration characteristics of simply supported beams, which is the most widely used bridge structure. The theoretical formula for the variation of the natural frequency of simply supported beams with temperature was proposed. The elastic modulus of simply supported beams in the range of -40 °C to 60 °C was acquired by means of the falling ball test and the theoretical formula and was compared with the elastic modulus obtained by the three-point bending test at room temperature (20 °C). In addition, the Midas/Civil finite-element simulation was carried out for the natural frequency of simply supported beams at different temperatures. The results showed that temperature was the main factor causing the variation of the natural frequency of simply supported beams. The linear negative correlation between the natural frequency of simply supported beams and their temperature were observed. The natural frequency of simply supported beams decreased by 0.148% for every 1 °C increase. This research contributed to the further understanding of the natural vibration characteristics of simply supported beams under the influence of temperature so as to provide references for natural frequency monitoring and damage identification of beam bridges.

Finite Element Modeling and Performance Evaluation of Piezoelectric Energy Harvesters with Various Piezoelectric Unit Distributions.

The piezoelectric energy harvester (PEH) is a device for recycling wasted mechanical energy from pavements. To evaluate energy collecting efficiency of PEHs with various piezoelectric unit distributions, finite element (FE) models of the PEHs were developed in this study. The PEH was a square of 30 cm × 30 cm with 7 cm in thickness, which was designed according to the contact area between tire and pavement. Within the PEHs, piezoelectric ceramics (PZT-5H) were used as the core piezoelectric units in the PEHs. A total of three distributions of the piezoelectric units were considered, which were 3 × 3, 3 × 4, and 4 × 4, respectively. For each distribution, two diameters of the piezoelectric units were considered to investigate the influence of the cross section area. The electrical potential, total electrical energy and maximum von Mises stress were compared based on the computational results. Due to the non-uniformity of the stress distribution in PEHs, more electrical energy can be generated by more distributions and smaller diameters of the piezoelectric units; meanwhile, more piezoelectric unit distributions cause a higher electrical potential difference between the edge and center positions. For the same distribution, the piezoelectric units with smaller diameter produce higher electrical potential and energy, but also induce higher stress concentration in the piezoelectric units near the edge.

Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors.

Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.

Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection.

As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.