The construction industry is undergoing a low-carbon and digital transformation. The application of recycled aggregates in 3D printed concrete has emerged accordingly, combining the advantages of solid waste recycling and automated manufacturing. However, the optimization of mix design and the prediction of mechanical properties for 3D printed recycled aggregate concrete (3DPRAC) face two main challenges, namely the micro defects inherent in recycled aggregates and the anisotropy caused by layered deposition. This study proposes a Gene Expression Programming (GEP)-based computational framework for the prediction of the splitting tensile strength (STS) of 3DPRAC. A dataset of 110 data points was generated, with layer height and loading direction as key printing parameters representing the anisotropic effect. GEP showed the best performance among models, with an R² of 0.914 on the testing set. Furthermore, the GEP model provides explicit mathematical equations that delineate the contributions of individual input variables and reveal the nonlinear effects of fiber content and printing parameters on STS.

This study addresses the interoperability of building information modeling (BIM) across different systems and platforms. Because the semantics of the industry foundation classes (IFC) standard are large and complex, traditional full semantic conversion methods are general-purpose, but they often lead to data expansion, which reduces system responsiveness and usability. In addition, the strong dependence of BIM models on specialized software further limits flexibility in practical use. Our novel framework combines a streamlined ontology with IFC, significantly improving memory efficiency and operational effectiveness compared to existing systems. Additionally, we integrate large language models for enhanced natural language processing in BIM data interactions. This harmonization of technologies not only simplifies system extension but also makes BIM data services more user-friendly and adaptable to various industry needs. By streamlining BIM data management and enriching data services, our approach broadens BIM’s applicability and improves data integration and extraction, establishing a more interactive and user-centric paradigm.

Background: Knowledge graphs (KGs) and Semantic web technologies (SWT) are increasingly explored in facilities management (FM) to address persistent problems of interoperability, fragmented data, and limited automation across operational systems.

Background: Knowledge graphs (KGs) and Semantic web technologies (SWT) are increasingly explored in facilities management (FM) to address persistent problems of interoperability, fragmented data, and limited automation across operational systems.

Methods: A scoping review was conducted in accordance with PRISMA-ScR 2020 to map KG applications in FM and identify emerging research directions. Web of Science and Scopus were queried for English-language, peer-reviewed studies applying ontologies, SWT, linked data, or KGs at building-level FM. Studies focused on non-FM contexts, earlier lifecycle phases without operation and maintenance relevance, or artificial intelligence and machine learning approaches without explicit SWT/KG integration were excluded. Following de-duplication and qualitative screening, 48 studies were thematically analysed.

Results: Four dominant application areas were identified: (1) ontology-driven semantic interoperability (n = 36), (2) systems integration across FM data sources (n = 24), (3) data and information retrieval from graph-based stores (n = 14), and (4) automated compliance and validation through knowledge-based reasoning (n = 6). Most studies employed modular, domain-specific ontologies deployed on graph databases to link static building information modelling and asset data with near real-time streams. Emerging directions include the use of KGs as semantic integration layers in digital twin (DT) architectures, and KG-grounded retrieval-augmented generation to enhance trustworthy and explainable access to FM information.

Conclusion: This review consolidates dispersed research on KG applications in FM and outlines a structured research agenda spanning modular ontology development, integration with DT and compliance workflows, as well as longer-term opportunities for scalable and auditable generative AI applications. Findings reflect peer-reviewed journal literature and may under-represent emerging industry practice.

This study addresses the challenges of solidifying high-moisture, high-alkalinity inorganic softening sludge (ISS) by utilizing alkali-activated slag-based cementitious materials. The research investigated strength evolution via unconfined compressive strength tests, while X-ray diffraction and scanning electron microscopy were employed to analyze phase composition and microstructural changes. Furthermore, response surface methodology was utilized to optimize mix proportions and explore interaction mechanisms. Results indicate that granulated blast furnace slag is the primary strength contributor, whereas fly ash and sodium silicate exhibit nonlinear effects. Microscopic analysis reveals that changes in raw material ratios influence strength development by regulating the formation of products such as calcium aluminosilicate hydrate (C-A-S-H) and sodium aluminosilicate hydrate (N-A-S-H). Optimization results from response surface methodology show that, when sodium hydroxide dosage is fixed at 2.5%, the optimal dosages for granulated blast furnace slag, fly ash, and sodium silicate are 30.65%, 29.98%, and 6.96%, respectively, achieving a 7-day unconfined compressive strength of 5.38 MPa. Furthermore, significant interactions exist between granulated blast furnace slag and fly ash, as well as between fly ash and sodium silicate. This study provides a theoretical basis for the resource utilization of ISS in road base materials.

With global warming, urban workers engaged in physically demanding occupations are increasingly exposed to severe heat stress. While the physical health risks are well recognized, less is known about how heat stress affects cognitive function at the neurophysiological level, an understanding critical for protecting worker well-being. This study conducted a controlled laboratory experiment to examine cognitive and neural responses to heat stress in middle-aged heat-exposed manual workers. Twenty participants (mid-40s) completed six representative cognitive tasks under four wet-bulb globe temperature (WBGT) conditions (23, 26, 28.5, and 31 °C). Electroencephalogram (EEG) recordings were used to monitor brain activity and assess changes in relative band power across δ, θ, α, and β frequency bands. The results revealed that performance in the executive- memory dimension peaked under moderate heat (forming an inverted-U pattern), while performance in the sensorimotor reactivity dimension remained stable or improved with rising WBGT. Neurophysiologically, rising heat stress led to increased δ band power but decreased α, θ, and β band powers. These EEG band power changes showed a nonlinear relationship with cognitive performance across both dimensions, with the left frontal cortex demonstrating higher sensitivity. Furthermore, topographical coupling maps indicated that executive-memory demanding tasks activated a more widespread cortical region than sensorimotor-reactivity demanding tasks. These findings show that graded heat stress alters brain dynamics and cognitive performance in a cognitive load-dependent manner, offering insights for designing adaptive work environments and real-time cognitive performance monitoring devices for heat- exposed workers.

The application of sustainable manufactured sand (MS) in 3D printed concrete is currently severely restricted by the “pumpability-buildability” conflict, primarily characterized by the high internal friction and angularity of MS particles. To overcome this bottleneck, this study proposes a novel process-oriented strategy: a secondary mixing protocol featuring a static resting period and delayed superplasticizer addition. This approach is explicitly designed to induce a “rheological rejuvenation” effect in stiff MS mixtures. Quantitative results demonstrate that this protocol reduces the static yield stress of high-volume MS ink (T-3) by approximately 39% (from 1,516 Pa to 924 Pa) and significantly diminishes thixotropic hysteresis, thereby successfully reopening the effective printing window to approximately 65 min. Although increasing MS content accelerates the structural build-up rate, the optimized 3D printed manufactured sand concrete (T-3) achieves a 28-day compressive strength of 61 MPa, representing a 15% enhancement over the river sand reference group (T-0). These findings confirm that the secondary mixing protocol is a robust solution capable of unlocking the potential of high-volume MS in sustainable digital construction.