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Pavements continually experience various types of distresses that are manifested in the form of defects that will eventually lead to the pavement’s failure. Selection of appropriate preventive maintenance strategies in terms of timing and method retards the large backlog of poor conditions of pavements, therefore improves the system-wide performance in an efficient and cost-beneficial way. However, the pre-treatment functional performance condition of asphalt pavement has a significant impact on the efficacy of preventive maintenance, and to provide years of service, it is necessary to designate an appropriate preventive maintenance method according to the actual functional condition of pavement. This study presents a new unsupervised learning approach for 1) extracting high correlation factors to reduce the dimensionality of feature distress data, and 2) accurately separating attributes that define pavement functional performance into severity levels to effectively assess the practiced preventive maintenance. Three unsupervised machine learning algorithms including feature extract, relevant factor analysis, and cluster analysis were executed. Based on the severity level results of the pre-treated pavement, the effects of four treatment methods (thin overlay, slurry seal, chip seal, and crack seal) were evaluated by a statistical analysis. The preventive maintenance data from the Specific Pavement Studies-3 (SPS-3) experiment of the Long-Term Pavement Performance (LTPP) database are used for the analysis. The results demonstrated that selecting the appropriate maintenance method for the functional performance risk level of the pavement is beneficial in achieving successful treatment results.

The early strength formation of Cement-Stabilized Macadam (CSM) base materials in cold regions is affected by environmental changes. At the same time, the curing temperature of CSM in the actual service environment did not meet the laboratory standard temperature curing environment (ST-CE). This study explores the evolution law of CSM performance and the influence of an early strength agent (ESA-I) on the early performance of CSM under variable temperature curing environment (VT-CE) in cold regions. The actual service environment for CSM curing was simulated in the laboratory, and CSM specimens with five ESA-I dosages of 0%, 5%, 10%, 15%, and 20% were prepared. The effect of ESA-I on the early properties of CSM under VT-CE was analyzed. The micro mechanism of CSM under different curing environments and ESA-I dosages was investigated based on X-ray diffraction, Fourier transform infrared spectroscopy, and Scanning electron microscope. The experimental results indicated that the early strength formation of CSM was slow in VT-CE, and the 7-day unconfined compressive strength did not meet the construction quality control indicators of CSM material under heavy traffic conditions. It was necessary to extend the curing period to meet the construction requirements. Adding ESA-I could enhance the strength, modulus, frost resistance, and fatigue performance of CSM under different curing environments. Compared with the ST-CE, the VT-CE would weaken the improvement effect of ESA-I on the performance of CSM. The recommended range for ESA-I in VT-CE was 10% −15% based on comprehensive compressive strength and resilient modulus indicators. Microscopic experiments show that under a VT-CE, the hydration rate of cement decreases, the Ettringite content in the hydration products is higher, and the microstructure is loose. Adding ESA-I improves the hydration rate of cement under VT-CE, and the microstructure contains more C-S-H hydration products.

Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with250mm×50mm×50mm, Uniaxial compression with 100mm×Φ100mm, Indirect tensile with63.5mm×Φ100mm,Four-point bending with 380mm×63.5mm×50mm), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (R2=0.99). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.

The purpose of this study is to further investigate the strength size effect and failure mechanism of asphalt mixtures and clarify the strength parameter conversion relationship between standard and non-standard size samples. The article established an improved microscopic model for uniaxial compression and indirect tensile testing of asphalt mixtures based on laboratory and discrete element simulation tests. The effects of thickness and gradation on the uniaxial compressive strength (UCS) and indirect tensile strength (ITS) of asphalt mixtures were studied. The loading rates of the UCS and ITS tests were set at 2 mm/min and 50 mm/min, respectively. Additionally, the tensile-compressive stress distribution, crack propagation, and strength contribution rates of different contact types within the sample were investigated during the virtual model loading process. The reliability of the model was validated through laboratory test results. Finally, the strength parameter conversion relationship between standard and non-standard size samples was investigated. The study found that the UCS of asphalt mixtures decreases with increasing thickness, while the ITS increases with increasing thickness, with average reduction and increase rates of 70.69 % and 24.18 %, respectively. The contact fracture ratio and the strength contribution rate of the aggregate-asphalt mortar contacts both exceed 50 %, indicating that the fracture of these contacts is the primary cause of asphalt mixture failure. The use of small-sized samples instead of standard samples in practical applications is promising. These research work outcomes can serve as a theoretical basis for designing asphalt pavement materials and acquiring existing asphalt pavement material parameters.

In view of the non-uniqueness of modulus selection and the mismatch among the values of various mechanical parameters of the same material in the current asphalt pavement design, typical pavement materials, asphalt mixture (AC), and cement stabilized macadam (CSM) were selected to perform unconfined compressive, direct tensile, bending, and splitting tensile tests. Then, the quantitative matching relationships among the mechanical parameters were established considering the velocity dependence and the different properties of tension and compression. Moreover, the superiority–inferiority of the four test methods was also evaluated by analyzing the stability and change law of test data and the specimens force characteristics. The results show that the difference in tension and compression of strength and modulus was the inherent attribute of the materials, which leads to the non-uniqueness of the mechanical parameters. The unconfined compressive and direct tensile tests are suitable methods to obtain mechanical parameters of pavement materials. The ratio of the direct tensile strength (modulus) to unconfined compressive strength (modulus) of AC decreases (increases) with the loading rate, while those of CSM shows an opposite regular pattern, and these above ratios of the two pavement materials tend to be a fixed value at higher loading rates. This research can provide a reference for the scientific selection and proper value of mechanical parameters in asphalt pavement design.

This study assessed the water stability of asphalt mixtures incorporating granite aggregates. A silane coupling agent was adopted to modify the surface of the granite aggregates, and scanning electron microscopy, contact angle measurements, Fourier transform infrared spectroscopy, and X-ray fluorescence were used to analyze the micro- and meso-characteristics of the aggregates. Effects of the silane treatment on the water stability of the mixtures were further evaluated by immersion and vacuum saturation Marshall tests and freeze–thaw splitting tests. The results indicated that the silane coupling agent had grafted onto the aggregate surface via chemical interactions, resulting in obviously raised contact angles and greatly reduced hydrophilicity. Additionally, the deposited organic film also reacted with the asphalt and formed a tighter bonding interface between the asphalt and aggregates, resulting in an asphalt mixture with a higher residual stability and splitting tension strength ratio when under environmental stress.

The lower radiation intensity (2-20W/m2) was widely used for asphalt ultraviolet (UV) aging. The effect of high-intensity radiation on asphalt properties and microscopic morphology is unknown. This study investigated the rheological behavior, microscopic characterization, and correlation analysis of SBS modified asphalt under high-intensity UV radiation. Rheological properties tests encompassed temperature sweep (TS), multiple stress creep recovery (MSCR), and frequency sweep (FS). Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Gel Permeation Chromatography (GPC) tests were performed to reveal the microscopic characterization. In the early stages of UV aging, the rutting factor of asphalt was increased. The increment decreased as the UV aging time increased. The UV aging process did not alter the temperature sensitivity of asphalt. The complex shear modulus was influenced by UV radiation duration and loading frequency. Extended exposure to high-intensity UV radiation resulted in the formation of microcracks on the surface of the asphalt. The maximum molecular weight of the UV-aged asphalt was higher than that of the original asphalt. The degraded SBS modifier deteriorated the homogeneity of asphalt. The sulfoxide aging index, carbonyl aging index, number-average molecular weight (Mn), weight average molecular weight (Mw), and polymer dispersity index (PDI) presented a better correlation with rheological behavior indexes. The correlation between small molecule size (SMS), medium molecule size (MMS), and large molecule size (LMS) with rutting factor and complex shear modulus was poor. It was worth mentioning that the correlation between the MSCR test results and microscopic performance indicators was the best. Especially for the recovery rate and Jnr at 3.2 kPa, the mean absolute value of the correlation coefficient was 0.953.

This study compared the performance of extracted asphalt and laboratory aged asphalt. The five-year service asphalt mixtures were used for extraction in this study. The laboratory aged asphalt experienced the Rolling thin film oven (RTFO) aging the Pressure aging vessel (PAV) aging with variable duration (5, 10, 15, 20, and 25hours). By comparing the softening point, viscosity, and rheological properties of both, an aging simulation protocol was established. For laboratory aged asphalt binder, the softening point increases by 3.56%, 5.68%, 7.43%, 8.16%, and 8.74% at PAV aging times of 5h, 10h, 15h, 20h, and 25h, respectively. The rutting index and viscosity of the aged asphalt gradually increases with the PAV duration. There is a substantial increase in creep recovery rate and a significant decrease in non-recoverable creep compliance for aged asphalt. The rheological aging index demonstrates a linear increasing trend. The sulfoxide (S=O) and aromatic (C=O) aging indexes of the laboratory-simulated aged asphalt progressively rise with prolonged PAV aging time. The S=O and C=O aging indexes of the extracted asphalt consistent with RTFO aged asphalt experienced PAV aging 20 and 25hours. The extracted asphalt can be simulated in the laboratory under standard RTFO conditions and a 22.26hours PAV duration at a pressure of 2.1MPa ± 0.1MPa. This study introduces a methodology for simulating asphalt aging in an authentic service environment by adjusting the laboratory aging test conditions.

Reaction temperature as a key parameter has been introduced to manipulate the film deposition of the CH3NH3PbI3absorber fabricated by the two-step solution deposition method. It is found that conversion time of dense CH3NH3PbI3layer can be significantly reduced by raising reaction temperature. CH3NH3PbI3crystal grain sizes increase with reaction temperature increasing, resulting in rougher surface. CH3NH3PbI3films deposited at higher temperatures exhibit better charge transport ability, larger built-in heterojunction field and weaker charge recombination, leading to enhanced solar cell performance. By optimizing reaction temperatures, as high as 17.40% and 14.02% of power conversion efficiencies of the mesoscopic and planar perovskite solar cells have been achieved, respectively.

A morphology-controlled CH3NH3PbI3-xClxfilm is synthesized via two-step solution deposition by spin-coating a mixture solution of CH3NH3Cl and CH3NH3I onto the TiO2/PbI2film for the first time. It is revealed that the existence of CH3NH3Cl is supposed to result in a preferential growth along the [110] direction of perovskite, which can improve both the crystallinity and surface coverage of perovskite and reduce the pinholes. Furthermore, the formation process of CH3NH3PbI3-xClxperovskite is explored, in which intermediates containing chlorine are suggested to exist. 13.12% of power conversion efficiency has been achieved for the mesoscopic cell, higher than 12.08% of power conversion efficiency of the devices fabricated without CH3NH3Cl via the same process. The improvement mainly lies in the increasing open-circuit photovoltage which is ascribed to the reduction of reverse saturation current density.

Hybrid organic–inorganic perovskites (e.g., CH3NH3PbI3) are promising light absorbers for the third-generation photovoltaics. Herein we demonstrate a modified two-step deposition method to fabricate a uniform CH3NH3PbI3capping layer with high-coverage and thickness of 300 nm on top of the mesoporous TiO2. The CH3NH3PbI3layer shows high light-harvesting efficiency and long carrier lifetime over 50 ns. On the basis of the as-prepared film, TiO2/CH3NH3PbI3heterojunction solar cells achieve a power conversion efficiency of 10.47% with a high open-circuit voltage of 948 mV, the highest recorded to date for hole-transport-material-free (HTM-free) perovskite-based heterojunction cells. The efficiency exceeding 10% shows promising prospects for the HTM-free solar cells based on organic lead halides.

A thin wide band gap organic semiconductor N,N,N′,N′-tetraphenyl-benzidine layer has been introduced by spin-coating to engineer the metal–semiconductor interface in the hole-conductor-free perovskite solar cells. The average cell power conversion efficiency (PCE) has been enhanced from 5.26% to 6.26% after the modification and a highest PCE of 6.71% has been achieved. By the aid of electrochemical impedance spectroscopy and dark current analysis, it is revealed that this modification can increase interfacial resistance of CH3NH3PbI3/Au interface and retard electron recombination process in the metal–semiconductor interface.

Mann iteration is weakly convergent in infinite dimensional spaces. We, in this paper, use the nearest point projection to force the strong convergence of a Mann-based iteration for nonexpansive and monotone operators. A strong convergence theorem of common elements is obtained in an infinite dimensional Hilbert space. No compact conditions are needed.