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.

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.

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.

Asphalt aging often leads to rapid degradation of road performance, which seriously affects the service life of asphalt pavement. Exploring the influence of asphalt oil sources, asphalt grades, and filler types on asphalt microcharacteristics in the asphalt aging process can provide an essential reference to guide asphalt pavement maintenance. In this study, we selected seven kinds of asphalt and three fillers commonly used in China for research. The pressurized aging vessel (PAV) and homemade ultraviolet (UV) aging equipment were used to perform thermo-oxidative aging and UV aging tests, respectively, of asphalt. The microcharacteristics of asphalt before and after aging were analyzed via attenuated total reflectance fourier transformation infrared spectroscopy and nuclear magnetic resonance ¹H spectroscopy. The results show that the oil source of asphalt exerted the most significant influence on the microcharacteristics of the aged asphalt, while the effect of the asphalt grade was relatively limited. The addition of fillers did not affect the aging mechanism of asphalt. UV and PAV aging generated apparent differences in the changes in the aged asphalt microstructure.