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Recent studies have highlighted the factors influencing the thermal performance of Pavement Solar Collectors (PSC), such as thermophysical properties of materials, geometrical specifications, and operational conditions. The present study introduces a new approach to investigating the impact of various parameters on the long-term performance of PSCs. The Response Surface Methodology (RSM) is used to optimise the experimental design by reducing the number of simulations resulting from the combination of several design parameters and ample design space. Hence, the proposed PSC system design aims to: i) assess the heat extraction capacity; ii) investigate the ability to diminish the asphalt surface temperature (STR); and iii) determine the reduction in asphalt layers' rutting potential (RTR), through a coupled RSM and Finite Element (FE) simulation framework. The proposed statistical prediction regression models for heat harvesting capacity, STR, and RTR, adequately represent the experimental data with predicted R2 values above 0.95. The pipe spacing, flow rate, and inlet supply temperature show a high sensitivity to the objective functions, while other parameters display a less sensitive response. Finally, a multi-objective optimisation framework using the NSGA-II is proposed to seek a Pareto front solution in the design space, considering different (or equal) weights for the objective functions. • A new approach introduced to investigate the impact of various design parameters. • Optimum design of PSC using coupled Response Surface Methodology and FE simulation framework. • Proposed statistical regression models for heat harvesting capacity and asphalt temperature. • The high sensitivity of pipe spacing, flow rate and inlet temperature to thermal responses. [ABSTRACT FROM AUTHOR]

In recent years, harvesting solar energy as a renewable and sustainable energy source has been studied extensively across engineering fields. Having reviewed more than 50 large-scale projects of Hydronic Asphalt Pavement (HAP), this paper offers a series of findings: the range of construction cost of asphalt collector varies between 25 and 151 €/m2 and 1.760–3.000 €/m2 for the heat exchanger and the total cost. The energy harvest capacity of asphalt solar collector systems (0,6–0,8 GJ/m2/year) and the required amount of heat for snow melting projects (100–900 W/m2) vary significantly in different projects. Using grid supports for easier pipe placement and protection of pipes against heavy loads during and after construction is recommended. Pavement solar collector systems reduced carbon dioxide emissions by 8–100% in different projects by changing their source of energy from fossil fuels to renewable and sustainable sources. Moreover, in order to further evaluate the sustainability of the HAP systems, a detailed life cycle assessment is required, including all available data related to the energy performance, pavement service life, material end-of-life recycling, etc. Finally, the paper identifies the knowledge gaps requiring further research especially in the area of energy output of the HAP systems, pavement service life and life cycle assessment. • A comprehensive review of large-scale hydronic asphalt pavement projects is provided. • Potential challenges in the large-scale hydronic asphalt pavement are investigated. • Technical specifications, operational aspects and performance are explored. • Construction cost and structural response of systems are analyzed. • The projects' performance is evaluated based on energy output and sustainability. [ABSTRACT FROM AUTHOR]

• Heating and cooling tests on a fully monitored large-scale pavement solar collector. • Thermal performance investigation of a controllable and configurable prototype. • Sensitivity analysis of design and operational parameters on system performance. • A great potential in reducing asphalt surface temperature to mitigate UHI effects. Solar energy harvesting as a renewable and sustainable energy source has been widely investigated in recent years across engineering fields. The use of Pavement Solar Collectors (PSC) can lead to clean energy production, an increase in road safety, prolong the service life of asphalt pavement, and can mitigate the Urban Heat Island (UHI) effect. This study describes a controllable large-scale research PSC prototype with high configuration flexibility, and full monitoring capability at the University of Antwerp, Belgium. Since small- or laboratory-scale setups do not reflect the behavior of actual projects, the present paper investigates the thermal response of a large-scale PSC in the Western European climate, including heating load, heat extraction capacity, and asphalt surface and profile temperature changes during heating and cooling experiments. The study shows that a low supply temperature compared to high (14 °C vs. 28 °C) can reduce the depletion rate of the stored thermal energy in borehole thermal energy storage up to 7 times. The sensitivity analysis indicates that an increase in flow rate from laminar to transient regime requires twice as much thermal power compared to the same flow rate changes within transient and turbulent regimes. The maximum average daily efficiency of the PSC could reach 34% with a flow rate of 4 l/min. The experimental results showed that increasing the pipe length from 50 m to 200 m reduces the cumulative power extraction capacity by up to 48%. Furthermore, the PSC system shows great potential in reducing the asphalt surface temperature (up to 12 °C) to mitigate the UHI effects. Finally, the PSC system effectively controls the temperatures of the interface zones to reduce the rutting distress in the summertime and lower the potential of cold thermal crack developments and brittle shear failure behavior in wintertime. [ABSTRACT FROM AUTHOR]

This study presents a novel methodology for estimating the master curve of the complex modulus of viscoelastic materials using a combination of optical measurement systems and ultrasonic excitation. Traditional techniques for characterizing properties of viscoelastic materials are often time-consuming and encounter limitations that hinder their accuracy at high strain rates. To address this, a method was proposed that leverages two optical measurement systems to quickly assess material properties at multiple points on a sample. A high-power ultrasonic transducer was employed to excite the material at its first longitudinal natural frequency, creating non-uniform temperature variations and strain rates. A scanning laser Doppler vibrometer measured vibrations across the material, enabling computation of the complex modulus magnitude under varying conditions. These results were correlated with temperature readings obtained from an infrared camera. The constructed master curve using the proposed methodology closely resembled those established through quasi-static and dynamic uniaxial compression tests in the literature. Additionally, this method revealed a more substantial increase in complex modulus at high strain rates compared to traditional experiments, where this characteristic is less pronounced. [Display omitted] • A new methodology was proposed for material characterization. • A high-power ultrasonic transducer was used for sample excitation. • CSLDV and IR thermography are integrated for vibration and temperature measurements. • Master curve of complex modulus was constructed using the novel methodology. [ABSTRACT FROM AUTHOR]

• Microstructure of a waxy bitumen is observed by a confocal laser scanning microscope. • A deep learning network is trained to detect the bee patterns in acquired images. • An image processing technique based on the two-dimensional fast Fourier transform is proposed. • Influence of short- and long-term ageing on the microstructure of a waxy bitumen is investigated. Bitumen, also called asphalt binder, is the key component in asphalt mixtures. Studies to investigate the microstructure of this material show a rich morphology, especially the formation of bee structures in bitumen containing wax. Most research in this field has investigated these microstructures using commercial image processing software that needs a manual selection of these patterns to obtain certain characteristics. This study aims to construct a deep-learning-based object-detection model that can detect these bee patterns in the images acquired from bitumen samples using Confocal Laser Scanning Microscopy (CLSM). The CLSM images are then used to determine the morphological properties of the samples. The properties investigated are some typical roughness parameters and the wavelength calculated by a novel image processing technique based on the two-dimensional fast Fourier transform. In addition, these developed methodologies are used to investigate the influence of short-term and long-term ageing on the microstructure of a waxy bitumen. The results show that the trained deep learning model can be used to successfully detect the location, number, and area of the bee structures. The number of bee patterns and the area of the surface they cover are reduced upon ageing. Furthermore, some strong trends are found between the computed roughness parameters and the ageing level of the samples. Finally, the estimated wavelength of the bee patterns increases by ageing bitumen. The successful development and demonstration of these methods show their great potential in analyzing the microscopic images of bitumen taken by CLSM or atomic force microscopy and the enormous opportunities for future research. [ABSTRACT FROM AUTHOR]