Ensuring consistent quality of recycled aggregates is essential for their wider use in circular construction. Laser-induced breakdown spectroscopy (LIBS) enables rapid elemental inspection, yet its performance in automated sorting systems is strongly shaped by how raw spectra are represented. This study adopts a representation-first benchmarking perspective and evaluates four representative feature families, namely variance-driven Principal Component Analysis (PCA), manifold learning-based Isomap, label-driven Partial Least Squares Discriminant Analysis (PLS-DA), and cepstral envelope-line separation, together with a raw-spectrum logistic-regression baseline and a histogram gradient boosting (HGB) reference model. The benchmark uses 24,000 single-shot spectra collected from ten material classes under conveyor-belt conditions, and repeated stratified random-split evaluation is used to assess the robustness of the comparative results across data splits. Across the evaluated models, cepstral features deliver the strongest overall performance, while PCA and the raw-spectrum logistic-regression baseline remain closely competitive. Isomap broadens the comparison toward non-linear manifold-based embeddings but does not improve performance in the present dataset, and PLS-DA shows the weakest stability under strong channel collinearity and class overlap. The results indicate that explicit feature extraction is not uniformly beneficial across all methods, but that spectral representation remains a major source of performance variation under controlled conveyor-like LIBS acquisition. In particular, cepstral features provide the most favourable balance among classification performance, robustness to baseline variation, and compactness, whereas PCA remains attractive when interpretability is prioritised. These findings provide a controlled benchmark and practical guidance for designing reliable and explainable LIBS-based quality-assurance pipelines for recycled-aggregate processing.

This study presents a method for recovering cement-rich powder from recycled fine aggregates by thermal shock, during which particles are fragmented and spalled due to differential thermal stress. When recycled fine aggregates (RFA) are exposed to high temperatures, the cement paste-rich boundary between the aggregates is weakened and spalled, liberating cement rich particles due to thermal shock. To investigate this phenomenon, experiments have been carried out by subjecting fine recycled aggregates to high temperatures ranging from 500 °C to 700 °C at different residence times. The result suggests that the particles split and crackle due to thermo-mechanical changes. Following thermal treatment, gentle milling completes the liberation process of recycled cement-rich powder (RCP). The composition of the recovered powder confirms the feasibility of the recovery method. To understand the thermo-mechanical process better, modelling efforts have been carried out on a spherical concrete particle of known diameter. The model predicts the temperature profile, residence time and radial stress inside the particle. According to the model, a 2 mm particle experiences a radial stress high enough to overcome the tensile strength of the concrete within 35 s, causing cracks due to the thermal gradient created between the inner and outer surfaces of the particle. These predictions have been verified by experimental results in the laboratory. This approach not only enhances recovery of RCP but also promotes sustainable construction practices.

Accurate failure prediction is critical to achieving Predictive Maintenance (PdM) for Indoor Air Quality (IAQ), which is highly related to resident well-being and operational effectiveness. However, most existing studies emphasise anomaly detection rather than prediction. To develop a precise and robust method for pre-emptive IAQ warning, this article integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Muti-Head Attention (MHA) mechanism into a novel C-B-M model, synergistically incorporating feature extraction, temporal dependency analysis, and contextual weighting mechanisms. Additionally, a real-world dataset collected from various buildings in Singapore is employed in a detailed comparative experiment with other benchmark models for different prediction periods, dataset selection, and failure severity levels to illustrate the effectiveness and robustness of the proposed method. Finally, a Digital Twin (DT)-oriented failure prediction framework for the indoor climate is introduced and validated through the prototype system demonstrating the 3D building model and IAQ alert information.

The recycling of municipal solid waste incineration (MSWI) bottom ash as a supplementary cementitious material (SCM) has attracted global attention, driven by the increasing availability of this by-product and the demand for sustainable SCMs to lower CO₂ emissions from cement production. Currently, the widespread use of MSWI bottom ash in the cement industry is hindered by the lack of guidelines to regulate material composition, optimize pretreatment processes, and specify mix design requirements. This review compiles and analyzes literature data on mix design, microstructural evolution, fresh properties, mechanical properties, durability, leaching risks, and environmental impacts of MSWI bottom ash-blended cement pastes, mortars, and concretes. The analysis aims to assess the influence of the pretreatment and physicochemical properties of bottom ash¹ on the microstructure and performance of blended cementitious materials.² The Ash Impact Strength Index (AISI) is introduced to quantify the effects of various factors on compressive strength, enabling direct comparison across different studies. Based on the statistical analysis of the 28-day AISI, the key quality requirements for MSWI bottom ash as an SCM are proposed, along with the optimal mix design. This work provides valuable insights and practical guidance to support the integration of bottom ash into the cement industry.

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.