Recycled aggregate concrete (RAC) is an environmentally friendly material. However, owing to inherent characteristics of the recycled aggregate (RA), it is difficult to promote and apply it in structural engineering. Silica fume (SF) and nano-silica (NS) have different characteristics as additives for RAC. It has been proven that adding SF only enhances the strength of RAC at a later stage, and NS can improve the early strength of RAC owing to its high pozzolanic activity. In this study, to further improve the properties of RAC, two types of additives were combined into RAC, which was named SF-NS-modified RAC (SSRAC). Compression and split tensile tests were conducted to analyze the mechanical properties of SSRAC at different curing ages. The results indicated that the combined addition of NS and SF improved the performance of RAC at early and later curing ages. Scanning electron microscopy and X-ray diffraction analyses were performed to explore the NS and SF mechanism. The results indicated that SF and NS in SSRAC had a good pozzolanic effect and underwent a secondary hydration reaction with calcium hydroxide to increase the production of calcium silicate hydrate, resulting in an increase in the properties of the interface transition zone. Finally, 6% SF and 2 or 3% NS are recommended as supplementary cementitious materials for RAC.

Bridge structural health monitoring (BSHM) has consistently been a research hotspot in civil engineering. The field of BSHM has experienced a significant transition from traditional manual inspections to an advanced integration of artificial intelligence (AI), culminating in the current peak with data‐driven AI methodologies. Nevertheless, despite the impressive performance, data‐driven AI techniques such as machine learning (ML) and DL exhibit limitations in interpretability, stability, and security. Conversely, the earlier generation of knowledge‐driven AI, including expert systems and logical reasoning, while offering greater interpretability and stability, has not achieved widespread adoption due to its limited scope, inefficiency, and subpar predictive accuracy. Against this backdrop, the current paper advocates for the creation of more reliable and intelligible explainable artificial intelligence (XAI). The paper provides a chronological overview of AI’s evolution within BSHM and discusses the fundamental principles of knowledge‐driven AI, data‐driven AI, and XAI. It examines their respective applications in BSHM and evaluates the advantages and limitations of these approaches. The paper concludes by anticipating future trends and identifying the challenges within the field. The findings underline the necessity for advancement in XAI in BSHM. The envisioned AI is designed to incorporate the advantages of both traditional knowledge‐driven AI and data‐driven AI while minimizing their respective shortcomings. This symbiosis is projected to set the direction for AI’s progression in BSHM.

The utilization of recycled aggregates made from construction wastes and recycled rubber made from waste tires is an effective method to realize the sustainable development. Thus, this study aims to determine the feasibility of using recycled aggregate concrete containing rubber, named rubberized recycled aggregate concrete (RRAC) as a new type of green-building material. The experimental carbon emissions test verified RRAC as a low-carbon material. In addition, the residual mechanical properties of RRAC were investigated under elevated temperatures. After exposure at 200, 400, and 600 C for 60 min, the stress−strain curve, compressive strength, energy absorption capacity, and spalling resistance of RRAC with recycled aggregate replacement ratios of 50 and 100%, rubber contents of 0, 5, 10, and 15% were explored with microstructural analysis. Moreover, empirical models were proposed to describe the effects of heated temperatures and rubber contents on the stress–strain relationship of RRAC. The results indicated that the rubber particles could reduce the spalling of specimens based on the vapor pressure theory. Therefore, this study provided scientific guidance for the design of structures made with RRAC for resisting high temperatures.

An innovative approach for measuring wind fields in urban building clusters using Unmanned Aerial Vehicles (UAVs) is presented. This method captures the distribution of wind fields within clusters. The results indicate that building architecture has a significant influence on wind flow characteristics at 15 m and 25 m height levels. Particularly, areas adjacent to the buildings and the wake section exhibit notable variations in wind speed and turbulence intensity compared to the incoming flow. The regions most affected include the areas flanking the buildings on either side and the intermediate section of the wake. The flow separation and convergence of incoming wind from the windward sides of the buildings notably amplify the wind load, resulting in a significant shift in wind speed and turbulence intensity within pedestrian pathways. The use of UAVs for wind measurements enables a flexible and efficient assessment of urban wind fields. These findings pave the way for further research into wind field measurements in urban architecture and a better understanding of the interference effects of buildings.