This doctoral dissertation explores the development and application of innovative technologies designed to improve the sustainability of the construction industry by effectively using recycled coarse aggregates (RCA). The primary aim of this research is to develop, implement, and validate novel methods that incorporate advanced technologies for accurate grading and quality assurance of RCA. By enhancing the efficiency of RCA repurposing, this work seeks to broaden its use in construction projects, significantly contributing to environmental sustainability.

The research begins by introducing a novel mobile system specifically designed to conduct on-site quality inspections of unscreened RCA streams. This technology provides an easily
transportable and efficient solution to assess and categorize RCA on-site, facilitating its immediate and effective reuse in various construction applications. This system leverages advanced technologies from the field of raw materials sorting and real-time data processing to ensure the quality and usability of recycled materials, aiming to reduce construction waste and enhance material lifecycle management.

The study further investigates the integration of RCA in the production of high-performance concrete through the industrial-scale implementation of intelligent optimal grading techniques. These techniques detail the integration of optimized grading algorithms that adjust the composition of RCA to enhance the mechanical properties of concrete. This methodology not only improves the quality of final concrete but also demonstrates the practical and scalable use of RCA in demanding construction environments.

A significant portion of the research is dedicated to the characterization of RCA using Laser-Induced Breakdown Spectroscopy (LIBS). This technique offers a quick and non-destructive way to accurately identify and classify materials and contaminants for in-line quality inspection of RCA. The precision and accuracy of LIBS allow for a detailed assessment of the RCA quality, crucial for ensuring the structural integrity and longevity of RCA-based concrete structures.

Further advancements are achieved by integrating LIBS with 3D scanning technologies. This combination establishes a more precise quality control system for RCA streams. By enhancing the detection and quantification of undesirable contaminants within RCA streams, this approach ensures that the materials in the final recycled products meet the required standards, thereby improving the overall reliability of RCA. This not only maintains but also improves the structural quality of the final concrete.

The dissertation concludes by synthesizing the technological innovations and research findings, emphasizing their implications for both the scientific community and the construction industry at large. It highlights the environmental benefits of adopting RCA, including reduced reliance on virgin materials and enhancing the sustainability of construction practices. Additionally, it outlines a series of future research directions that focus on refining these technologies, exploring their economic impacts and commercial viability, and evaluating the long-term performance of structures built with RCA concrete.

Overall, this thesis provides a substantial contribution to the field of sustainable construction, offering practical, technology-driven solutions that pave the way for a more sustainable and environmentally conscious construction industry. The methodologies developed herein not only push the boundaries of academic research but also present viable, industry-ready applications that can significantly impact the way construction materials are recycled and utilized.@en

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

To upcycle End-of-Life (EoL) concrete from demolished buildings, it is essential to efficiently identify the different materials that may contaminate it. The precise identification and classification of materials and contaminants are vital processes for in-line quality inspection of recycled concrete aggregates transported on a conveyor belt. In this study, a total of eight potential contaminants are considered as target contaminant materials in the streams made of coarse and fine aggregates resulting from the upcycling of EoL concrete. These contaminants degrade the quality of the aggregates even at low concentrations, so it is essential to identify the presence of such contaminants along with the main products of recycling which are recycled coarse aggregates (RCA) and recycled fine aggregates (RFA). An efficient method is proposed to identify and classify EoL concrete waste along with RCA and RFA in motion on conveyor belts via laser-induced breakdown spectroscopy (LIBS) coupled with a cluster-based identification algorithm. The model is verified with an accuracy of 0.97, a precision (weighted average) of 0.98, a recall (weighted average) of 0.97, and an F1-score (weighted average) of 0.98 for the validation set, under the optimal conditions. This study suggests that LIBS may be well suited for fast and in-line analysis of recycled concrete aggregates in industrial applications. This approach presents an innovative approach for the quality characterization of secondary materials produced from EoL concrete being transported on conveyor belts, and therefore can be of great value for the processing and high-end utilization of EoL concrete.

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