Recycled coarse aggregates (RCA) from End-of-Life (EoL) concrete face resistance due to inconsistent quality. To address this, a mobile, containerized sensor-based inspection system is developed, capable of processing over 100 tons of RCA per hour. Using advanced 3D scanning and laser-induced breakdown spectroscopy (LIBS), the system ensures reliable real-time analysis of particle size distribution (PSD) (Root Mean Square Error: <5.5%) and contaminant detection (Accuracy: 0.94). Incremental learning techniques dynamically update chi-square distribution parameters as new spectral data becomes available, refining models continuously without full retraining and sustaining high classification performance. Monitoring data are recorded on radio frequency identification (RFID) tags, enhancing traceability. This innovation improves efficiency compared to traditional methods, supporting sustainable practices in the construction industry. Its applications also extend to related fields such as mining, waste management, and resource recovery, contributing to the circular economy, reducing reliance on natural aggregates, and promoting environmentally friendly infrastructure development.@en

Recycling coarse aggregates from construction and demolition waste is essential for sustainable construction practices. However, the quality of recycled coarse aggregates (RCA) often fluctuates significantly, in contrast to the more stable quality of natural aggregates. Contaminants in RCA notably compromise its quality and usability. Therefore, automating the quality control of RCA is necessary for the recycling industry. This study introduces an industry-focused, innovative, and rapid quality control system that combines Laser-Induced Breakdown Spectroscopy (LIBS) with 3D scanning technologies to enhance the detection of contaminants in RCA streams. The system involves a synchronized application of LIBS for spectral analysis and 3D scanning for the physical characterization of different materials. Results reveal that the dependability of single-shot LIBS analysis has been enhanced, thus elevating the precision of contaminant detection. This improvement is achieved by accounting for the laser shot's angle of incidence and focal length adjustments. The introduced technology holds potential for application in the real-time examination of substantial volumes of RCA, facilitating a rapid and reliable quality control method. This rapid assessment technique delivers online data about the concentration of contaminants in RCA, including recycled fine aggregates, cement paste, bricks, foam, glass, gypsum, mineral fibers, plastics, and wood. This data is both essential and sufficient for choosing a cost-effective mortar recipe and guaranteeing the performance of the final concrete product in terms of strength and durability in construction projects. The system can monitor the quality of RCA flows at throughputs of 50 tons per hour per conveyor, characterizing approximately 4000 particles in every ton of RCA, in this way signaling the most critical contaminants at levels of less than 50 parts per million. With these characteristics, the system could also become relevant for other applications, such as characterizing mining waste or solid biofuels for power plants.@en

The number of installations of PV modules in residential areas has been increasing rapidly over the last years. Often PV modules are installed on rooftops of buildings in a complex urban environment, in which nearby buildings may cast shade on the modules for some period of time. In this contribution we present a method to calculate the direct solar irradiation on building surfaces taking into account the shading. This irradiation is required to work out the energy yield of PV modules mounted on these surfaces. The calculation is realized by taking the ratio between the direct annual solar irradiation a surface receives taking shading into account, and the potential direct annual solar irradiation without shading. We denote this ratio as the shading factor. For this method we make use of freely available software with which position and height information of surrounding buildings can be constructed, and with which the shade of these buildings on the building under investigation can be calculated. By applying this method over the entire year, the shading factor can be obtained for all building surfaces. The method has been applied to the building of the Faculty of Electrical Engineering, Mathematics and Computer Science of Delft University of Technology. We demonstrate that the shading cast by buildings and other obstacles is calculated accurately. Comparisons with measurements show that the method predicts the shading factor very well, allowing an accurate estimation of PV system electricity yield.@en