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1. Data Compression Approach for Long-Term Monitoring of Pavement Structures

   https://doi.org/10.3390/infrastructures5010001  

   Manosalvas-Paredes, Mario; Lajnef, Nizar; Chatti, Karim; Aono, Kenji; Blanc, Juliette; Thom, Nick; Airey, Gordon; Lo Presti, Davide (January 2020, Infrastructures)

   null (Ed.)

   Pavement structures are designed to withstand continuous damage during their design life. Damage starts as soon as the pavement is open to traffic and increases with time. If maintenance activities are not considered in the initial design or considered but not applied during the service life, damage will grow to a point where rehabilitation may be the only and most expensive option left. In order to monitor the evolution of damage and its severity in pavement structures, a novel data compression approach based on cumulative measurements from a piezoelectric sensor is presented in this paper. Specifically, the piezoelectric sensor uses a thin film of polyvinylidene fluoride to sense the energy produced by the micro deformation generated due to the application of traffic loads. Epoxy solution has been used to encapsulate the membrane providing hardness and flexibility to withstand the high-loads and the high-temperatures during construction of the asphalt layer. The piezoelectric sensors have been exposed to three months of loading (approximately 1.0 million loads of 65 kN) at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) fatigue carrousel. Notably, the sensors survived the construction and testing. Reference measurements were made with a commercial conventional strain gauge specifically designed for measurements in hot mix asphalt layers. Results from the carrousel successfully demonstrate that the novel approach can be considered as a good indicator of damage progression, thus alleviating the need to measure strains in pavement for the purpose of damage tracking. 

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2. Lab-Controlled Experimental Evaluation of Heat-Reflective Coatings by Increasing Surface Albedo for Cool Pavements in Urban Areas

   https://doi.org/10.3390/coatings12010007  

   Lu, Yang; Rahman, Md Asif; Moore, Nicholas W.; Golrokh, Aidin J. (January 2022, Coatings)

   Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was pre-viously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of per-formance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas. 

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3. Lab-Controlled Experimental Evaluation of Heat-Reflective Coatings by Increasing Surface Albedo for Cool Pavements in Urban Areas

   https://doi.org/doi.org/10.3390/coatings12010007  

   Lu, Yang; Rahman, Md Asif; Moore, Nicholas W.; Golrokh, Aidin J. (December 2021, Coatings)

   Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was pre-viously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of per-formance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas. 

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4. Nano-Scale and Macro-Scale Characterizations of the Effects of Recycled Plastics on Asphalt Binder Properties

   https://doi.org/10.3390/buildings14030642  

   Al-Hosainat, Ahmad; Nazzal, Munir D; Kaya, Savas; Reza, Toufiq (March 2024, Buildings)

   This paper summarizes the results of one of the first comprehensive laboratory studies that was conducted to evaluate the effects of adding different contents of recycled polyethylene terephthalate (rPETE) as a modifier to an asphalt binder on the rheological and mechanical properties of the modified binder as well as on the agglomeration behavior between the rPETE and asphalt binder at a multiscale level. The high-temperature and low-temperature performances of the modified binder were investigated at the macro-scale and compared with those of the unmodified binder using dynamic shear rheometer (DSR) and bending-beam rheometer (BBR) rheological tests, as well as asphalt binder cracking device (ABCD) testing. The nano-scale evaluation of the binder properties, including the surface roughness, bonding energy, and reduced modulus, was accomplished using atomic force microscopy (AFM). The results indicated that the addition of rPETE enhanced the high- and intermediate-temperature rheological properties of the modified PG 64-22 binder. The low-temperature rheological properties and resistance to cracking decreased slightly with increasing rPETE content in the asphalt binder. However, this reduction was not remarkable when adding 4%, 8%, and 10% rPETE contents. The asphalt binder modified with 4% rPETE had a low-temperature grade of −22, similar to that of the unmodified binder, indicating that 4% rPETE can be added to the binder to improve its high- and intermediate-temperature properties without reducing its resistance to low-temperature damage. The AFM tapping-mode results indicated that the inclusion of rPETE in the asphalt binder improved the stiffness properties of the modified binder as compared with those of the control asphalt binder. In addition, the rPETE-modified binders showed rougher surfaces than the control binder. The addition of rPETE to the binder increased the values of the reduced modulus and bonding energy compared with those of the control binder. 

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5. Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images

   https://doi.org/10.3390/s25041145  

   Sanchez, Carlos; Wang, Feng; Bai, Yongsheng; Gong, Haitao (February 2025, Sensors)

   Pavement surface distresses are analyzed by transportation agencies to determine section performance across their pavement networks. To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. However, the precision of this novel technology often leads to inaccuracies that must be verified by pavement engineers. Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. With the use of 2D/3D pavement images, surface distress analysis can help train models to efficiently detect and classify surface distresses that may be caused by traffic loading, weather, aging, and other environmental factors. The formation of these distresses is developing at a higher rate in coastal regions, where extreme weather phenomena are more frequent and intensive. This study aims to develop a YOLOv5 model with 2D/3D images collected in the states of Louisiana, Mississippi, and Texas in the U.S. to establish a library of data on pavement sections near the Gulf of Mexico. Images with a resolution of 4096 × 2048 are annotated by utilizing bounding boxes based on a class list of nine distress and non-distress objects. Along with emphasis on efforts to detect cracks in the presence of background noise on asphalt pavements, six scenarios for augmentation were made to evaluate the model’s performance based on flip probability in the horizontal and vertical directions. The YOLOv5 models are able to detect defined distresses consistently, with the highest mAP50 scores ranging from 0.437 to 0.462 throughout the training scenarios. 

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