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Peng Lin
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8 records found
1
Accurate and efficient inspection of road surface defects is essential for ensuring traffic safety and supporting timely maintenance. However, existing vision-based inspection techniques often rely on high-resolution cameras or laser sensors, which limit robustness under complex or dynamic environmental conditions. To overcome these challenges, this study develops a vehicle-mounted acoustic inspection framework based on tire–road coupling noise for rapid road surface condition assessment. An acoustic inspection dataset is established, covering multiple types of urban pavement defects under diverse traffic and environmental conditions. The captured noise signals are transformed into MFCC, which serve as frequency-domain features for defect detection. A deep neural network integrating CNN, SE attention, and BiLSTM modules is developed to extract multi-scale time-frequency features and model temporal dependencies from tire-road coupling noise for pavement defect detection. Model evaluation results demonstrate that the proposed CNN-SE-BiLSTM model can accurately classify pavement defects without reliance on visual sensors, achieving an overall F1-score of 84.0%, while maintaining real-time inference and strong robustness to varying road surface conditions. Compared with existing vision- and Sensor- based inspection methods, the proposed method offers advantages in lower deployment cost, reduced sensitivity to illumination and weather variations, and easier integration into standard vehicles for continuous large-scale inspection. A field experiment on urban roads verifies the effectiveness of the proposed road surface inspection method, and a total of 12 pavement defects, including 9 cracks and 3 potholes, were successfully identified under real driving conditions. It is concluded that the proposed tire-road coupling noise method provides a cost-effective solution for road surface inspection.
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Accurate and efficient inspection of road surface defects is essential for ensuring traffic safety and supporting timely maintenance. However, existing vision-based inspection techniques often rely on high-resolution cameras or laser sensors, which limit robustness under complex or dynamic environmental conditions. To overcome these challenges, this study develops a vehicle-mounted acoustic inspection framework based on tire–road coupling noise for rapid road surface condition assessment. An acoustic inspection dataset is established, covering multiple types of urban pavement defects under diverse traffic and environmental conditions. The captured noise signals are transformed into MFCC, which serve as frequency-domain features for defect detection. A deep neural network integrating CNN, SE attention, and BiLSTM modules is developed to extract multi-scale time-frequency features and model temporal dependencies from tire-road coupling noise for pavement defect detection. Model evaluation results demonstrate that the proposed CNN-SE-BiLSTM model can accurately classify pavement defects without reliance on visual sensors, achieving an overall F1-score of 84.0%, while maintaining real-time inference and strong robustness to varying road surface conditions. Compared with existing vision- and Sensor- based inspection methods, the proposed method offers advantages in lower deployment cost, reduced sensitivity to illumination and weather variations, and easier integration into standard vehicles for continuous large-scale inspection. A field experiment on urban roads verifies the effectiveness of the proposed road surface inspection method, and a total of 12 pavement defects, including 9 cracks and 3 potholes, were successfully identified under real driving conditions. It is concluded that the proposed tire-road coupling noise method provides a cost-effective solution for road surface inspection.
Accurate segmentation and quantitative characterization of pavement cracks are critical for road condition assessment and preventive maintenance. However, existing methods often lack robustness under complex field conditions, leading to inaccurate estimation of crack length and width. In this study, a two-step method is proposed for automatic crack segmentation and characterization using a vehicle-mounted system. Firstly, a high-resolution pavement dataset comprising 10,348 images, augmented with diverse environmental conditions, is established for model training and evaluation. Secondly, an improved SegFormer network with coordinate attention is trained and employed to enhance crack boundary preservation and suppress background noise in segmentation. Thirdly, an improved A* algorithm integrated with a dynamic window approach (DWA) is applied to extract continuous crack centerlines and adaptively compute length and width through perpendicular distance measurements. Experimental results demonstrate that the proposed method achieves superior performance with an accuracy of 98.74%, mPA of 85.79%, and inference speed of 149 frames per second, outperforming traditional segmentation model. Field validation further confirms that the relative error of crack length and width estimation is lower than 10%. These findings indicate that the proposed two-step method provides an accurate, efficient, and robust solution for real-time pavement crack characterization in practical road inspection scenarios.
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Accurate segmentation and quantitative characterization of pavement cracks are critical for road condition assessment and preventive maintenance. However, existing methods often lack robustness under complex field conditions, leading to inaccurate estimation of crack length and width. In this study, a two-step method is proposed for automatic crack segmentation and characterization using a vehicle-mounted system. Firstly, a high-resolution pavement dataset comprising 10,348 images, augmented with diverse environmental conditions, is established for model training and evaluation. Secondly, an improved SegFormer network with coordinate attention is trained and employed to enhance crack boundary preservation and suppress background noise in segmentation. Thirdly, an improved A* algorithm integrated with a dynamic window approach (DWA) is applied to extract continuous crack centerlines and adaptively compute length and width through perpendicular distance measurements. Experimental results demonstrate that the proposed method achieves superior performance with an accuracy of 98.74%, mPA of 85.79%, and inference speed of 149 frames per second, outperforming traditional segmentation model. Field validation further confirms that the relative error of crack length and width estimation is lower than 10%. These findings indicate that the proposed two-step method provides an accurate, efficient, and robust solution for real-time pavement crack characterization in practical road inspection scenarios.
In order to explore the feasibility and efficacy of reed-fiber-modified bitumen (RFMB), three lengths and three dosages of reed fibers were selected to modify bitumen and bituminous mortar, while the physicochemical properties of RFMB and RFMB mortar were analyzed. In this work, FTIR spectroscopy was employed to characterize the chemical impact of fiber on bitumen. The viscidity and rheology of RFMB and the tensile strength of RFMB mortar were evaluated using a Brookfield viscometer, dynamic shear rheometer, and monotonic tensile test. The results showed that adding fibers primarily affects the physical structure rather than the chemical composition of bitumen, confirmed by FTIR spectroscopy. RFMB viscosity increased with higher fiber dosage and fiber length. Rheological evaluations showed an enhanced complex shear modulus for RFMB, suggesting improved performance at higher temperatures but increased stiffness at lower temperatures, with the latter indicating reduced flexibility. RFMB also demonstrated superior fatigue and rutting resistance, albeit with compromised stress sensitivity. Tensile tests on RFMB mortar highlighted significant improvements, especially with longer fibers, while shorter 0.4 mm fibers showed modest reinforcement effects, possibly due to uneven distribution during sample preparation.
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In order to explore the feasibility and efficacy of reed-fiber-modified bitumen (RFMB), three lengths and three dosages of reed fibers were selected to modify bitumen and bituminous mortar, while the physicochemical properties of RFMB and RFMB mortar were analyzed. In this work, FTIR spectroscopy was employed to characterize the chemical impact of fiber on bitumen. The viscidity and rheology of RFMB and the tensile strength of RFMB mortar were evaluated using a Brookfield viscometer, dynamic shear rheometer, and monotonic tensile test. The results showed that adding fibers primarily affects the physical structure rather than the chemical composition of bitumen, confirmed by FTIR spectroscopy. RFMB viscosity increased with higher fiber dosage and fiber length. Rheological evaluations showed an enhanced complex shear modulus for RFMB, suggesting improved performance at higher temperatures but increased stiffness at lower temperatures, with the latter indicating reduced flexibility. RFMB also demonstrated superior fatigue and rutting resistance, albeit with compromised stress sensitivity. Tensile tests on RFMB mortar highlighted significant improvements, especially with longer fibers, while shorter 0.4 mm fibers showed modest reinforcement effects, possibly due to uneven distribution during sample preparation.
This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. The simulations cover three bitumen types (NO, TO, FO), five aging degrees, and four temperatures (60 °C, 120 °C, 160 °C, 200 °C), capturing diffusion coefficients ranging from 0.0068e-10 m2/s in highly aged bitumens at 60 °C to 4.35e-10 m2/s in fresher samples at 200 °C. The MLM, built with 18 chemical descriptors for bitumen and rejuvenator sides, achieves an R2 of 0.97, accurately predicting diffusion across varied conditions. This approach abstracts away from the need for repeated MD simulations, enabling diffusion predictions even for systems outside the original dataset. The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. It also demonstrates how the model offers a practical framework for understanding the diffusion and performance of rejuvenators by linking time-dependent factors—such as concentration, depth, and rejuvenation time—with the bulk properties of bitumen-rejuvenator systems, facilitating industrial applications.
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This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. The simulations cover three bitumen types (NO, TO, FO), five aging degrees, and four temperatures (60 °C, 120 °C, 160 °C, 200 °C), capturing diffusion coefficients ranging from 0.0068e-10 m2/s in highly aged bitumens at 60 °C to 4.35e-10 m2/s in fresher samples at 200 °C. The MLM, built with 18 chemical descriptors for bitumen and rejuvenator sides, achieves an R2 of 0.97, accurately predicting diffusion across varied conditions. This approach abstracts away from the need for repeated MD simulations, enabling diffusion predictions even for systems outside the original dataset. The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. It also demonstrates how the model offers a practical framework for understanding the diffusion and performance of rejuvenators by linking time-dependent factors—such as concentration, depth, and rejuvenation time—with the bulk properties of bitumen-rejuvenator systems, facilitating industrial applications.
This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.
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This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.
PDB2DAT
Automating LAMMPS data file generation from PDB molecular systems using Python, Rdkit, and Pysimm
Pdb2dat, developed in Python, is an open-source, self-contained utility that facilitates the conversion of PDB files into LAMMPS data files, catering to the need of initializing atomistic simulation from initial atomic configurations. It extracts molecular details from PDB files, uses Rdkit and Xyz2mol for bonding analysis and 3D conformer generation, and uses Pysimm for assigning force field types and charges. Designed to be lightweight and fully Pythonic, pdb2dat is suitable for use in privilege-limited high-throughput environments. The output details system topologies for use in MD simulations, significantly simplifying the preparatory steps needed by researchers to explore materials phenomena through LAMMPS.
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Pdb2dat, developed in Python, is an open-source, self-contained utility that facilitates the conversion of PDB files into LAMMPS data files, catering to the need of initializing atomistic simulation from initial atomic configurations. It extracts molecular details from PDB files, uses Rdkit and Xyz2mol for bonding analysis and 3D conformer generation, and uses Pysimm for assigning force field types and charges. Designed to be lightweight and fully Pythonic, pdb2dat is suitable for use in privilege-limited high-throughput environments. The output details system topologies for use in MD simulations, significantly simplifying the preparatory steps needed by researchers to explore materials phenomena through LAMMPS.
Review
(2024)
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Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman, Qu Yang, Anna Giordano, Julie Grollier, Hyunsoo Yang, Sorin D. Cotofana, Peng Lin, More Authors...
In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.
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In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.
Journal article
(2019)
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Peng Lin, Chuanqi Yan, Weidong Huang, Yi Li, Lu Zhou, Naipeng Tang, Feipeng Xiao, Yi Zhang, Quan Lv
To meet the high temperature and anti-reveling properties required in open-graded friction course, high content polymer modified asphalt (HCPMA) is gradually widely used in China, but the rheological, chemical and aging characteristic is not clear yet. In this paper, HCPMA with different SBS content and different base asphalt are prepared, and Pressure Aging Vessel (PAV) aging was conducted to simulate the long-term aging condition. The chemical and rheological evaluation of HCPMA before and after aging were tracked with Fourier transform infrared, gel permeation chromatography test, dynamic shear oscillatory test, master curve and multiple stress creep and recovery test. The results show that firstly, the aging of HCPMA is combined with hardening of asphalt and degradation of Styrene–butadiene–styrene (SBS) polymer. Furthermore, the addition of high content of SBS polymer can reduce the formation of carbonyl, but the degradation rate of SBS polymer is not related to the content of SBS or the type of base asphalt. Besides, HCPMA with a higher SBS content will have better rheological properties, but in consideration of economy, 9% is optimum dosage. At last, HCPMA prepared with Esso asphalt as base binder exhibits better rheological properties than HCPMA prepared with SK asphalt. However, the rheology difference reduces with the increase of SBS content and after PAV aging.
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To meet the high temperature and anti-reveling properties required in open-graded friction course, high content polymer modified asphalt (HCPMA) is gradually widely used in China, but the rheological, chemical and aging characteristic is not clear yet. In this paper, HCPMA with different SBS content and different base asphalt are prepared, and Pressure Aging Vessel (PAV) aging was conducted to simulate the long-term aging condition. The chemical and rheological evaluation of HCPMA before and after aging were tracked with Fourier transform infrared, gel permeation chromatography test, dynamic shear oscillatory test, master curve and multiple stress creep and recovery test. The results show that firstly, the aging of HCPMA is combined with hardening of asphalt and degradation of Styrene–butadiene–styrene (SBS) polymer. Furthermore, the addition of high content of SBS polymer can reduce the formation of carbonyl, but the degradation rate of SBS polymer is not related to the content of SBS or the type of base asphalt. Besides, HCPMA with a higher SBS content will have better rheological properties, but in consideration of economy, 9% is optimum dosage. At last, HCPMA prepared with Esso asphalt as base binder exhibits better rheological properties than HCPMA prepared with SK asphalt. However, the rheology difference reduces with the increase of SBS content and after PAV aging.