This study investigates the optimization of urban ventilation in Zhao’an Old Town, Fujian, through the integration of Computational Fluid Dynamics (CFD) simulations and field measurements. The findings underscore the critical roles of spatial layout indicators, such as the Frontal Area Index (FAI), Opening-to-Facade Ratio (OFR), and Building Volume Density (BVD), in influencing wind flow and thermal performance. The FAI was identified as the most influential factor in shaping airflow, while the OFR and BVD highlighted the importance of open spaces and balanced building density for natural ventilation and thermal comfort. Practical strategies, such as optimizing building orientations, increasing facade permeability, and leveraging natural cooling elements like the Dongxi River, are proposed to address ventilation challenges while preserving the town’s cultural and historical integrity. Unlike previous studies, this research combines CFD simulations with summer field measurements to provide a highly accurate and contextually relevant evaluation of wind flow dynamics in compact urban environments. By systematically analyzing the interplay between urban morphology and ventilation efficiency, this study offers actionable recommendations for improving outdoor comfort in heritage settings. The outcomes serve as a valuable reference for sustainable urban planning, contributing to the development of strategies that balance environmental performance with the preservation of Zhao’an Old Town’s unique cultural heritage.

With the globalization of the world economy and the integration and heterogeneity of cultures, the collision between traditional settlements and local traditional culture, traditional culture and modern culture is gradually reduced. Traditional cultures, traditional settlements, and traditional architectural forms have gradually declined. Therefore, in the context of globalization, people are more concerned about how to recognize, understand, and inherit these traditions and traditional ways of life in the context of today’s society and how to combine with the needs of contemporary communities to create an outdoor space suitable for human survival. However, due to the lack of research on traditional wind-heat conditions, there is no feasible evaluation method. Taking a typical village in Lianjiang County, Fuzhou as an example, various factors affecting wind-heat conditions in traditional villages are discussed in this paper. The CFD simulation technology is used to simulate and compare various types of settlements, and the wind and thermal environment around are compared and evaluated in detail and carried out in-depth research on it. By summarizing the general rules of natural ventilation of traditional residential buildings in Beigan Township, Lianjiang County, and Fuzhou, it is expected to be helpful to today’s ecological construction. In order to construct a new type of energy-saving and land-saving community, some feasible methods and ideas are put forward to make it more realistic.

Recycled coarse aggregates (RCA) offer an alternative to natural coarse aggregates in concrete production, reducing natural aggregate extraction and landfill burdens and potentially lowering embodied energy and CO₂ emissions. This study leverages machine learning algorithms to predict the dynamic yield stress (DYS) and plastic viscosity (PV) of RCA concrete (RCAC). A database of 380 RCAC mixtures, incorporating 11 input features, was analyzed using six machine learning models: Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM). The model performance was compared, followed by sensitivity analyses to identify critical factors influencing DYS and PV. For DYS, the DT model demonstrated the highest predictive performance (testing R²/RMSE/MAE = 0.95/18.25/13.99; others: 0.90–0.93/12.14–26.10/15.40–19.50) due to its robustness on smaller datasets. The XGBoost model led for PV (testing R²/RMSE/MAE = 0.93/7.06/4.58; others: 0.82–0.89/8.69–11.20/6.06–7.51) owing to its sequential residual minimization that captures nonlinear interactions. Sensitivity analyses revealed that polycarboxylate superplasticizer content and water-to-binder ratio significantly influence DYS, while cement content and saturated-surface-dried water absorption of RCA (i.e., measured with open pores filled and the aggregate surface dry) dominate PV. The time-dependent role in affecting PV was also highlighted. By optimizing and comparing different machine learning algorithms, this study advances predictive methodologies for the rheological properties of RCAC, addressing the underexplored use of machine learning for RCAC rheology (DYS and PV) and the limitations of traditional empirical rheology methods, thereby promoting the efficient use of recycled materials in sustainable concrete design.

Three-dimensional (3D) modelling has become essential in the heritage field, but static models cannot capture how heritages evolve over time. To this end, more and more studies have presented four-dimensional (4D, i.e., 3D + time) applications. However, there is no systematic overview. Hence, this study provides a comprehensive review of 4D technologies applied to heritage. Through a two-stage Web of Science retrieval, 56 studies (2000–2025) were analysed, covering 3D and heritage building information modelling (HBIM) construction, time incorporation, calibration, and interaction. This review explicitly focuses on how time is incorporated into survey-anchored 3D and HBIM models, and how calibration ensures the reliability of diachronic interpretation. The review distinguishes between survey-anchored 3D and HBIM pipelines, outlines diachronic modelling and calibration workflows, and summarises emerging extensions such as depth sensing, temporal lensing, and time traceback. Moreover, it also provides a mini-review of five-dimensional (5D) applications and their future directions inspired by the 4D overview. The specific contributions of this study are threefold: (i) it synthesises a complete and comparable workflow for integrating time into ordinary 3D models and HBIM; (ii) it formalises calibration principles and reportable metrics for trustworthy temporal reconstruction; and (iii) it clarifies how emerging 5D extensions build on 4D practice rather than replace it.

This study sets out to improve the indoor and outdoor air environments of Historically Preserved Buildings through digital management, achieving energy savings and emission reduction. Historically, environmental control in art museums relied on manual aids and airtight construction, leading to significant energy consumption and complete Monument isolation. However, advancements in artificial intelligence have enabled the digital management of healthy environments.This study utilizes a literature review, case studies, and computational fluid dynamics (CFD) . a literature review was conducted to collect data on active and passive Monuments, healthy art museum environments, and digital governance of Monument and environmental control. The National Taiwan Museum of Fine Arts (NTMA), Museums of Historical Preservation, was selected as the primary research target, and data was collected through field surveys, aerial photography, and construction of a three-dimensional model. wind environment simulation and thermography analysis were conducted through PHOENICS and PIX4D, and a proposal was made.This study yielded the following two conclusions about NTMFA:1. Direct isolation of the physical environment control and aesthetics-oriented external planting methods make it difficult to reduce the indoor temperature and improve the air quality. These methods are also very energy-consuming；2. The NTMFA employs four strategies to enhance indoor and outdoor spaces, passive and active design, area redrawing, digital management of active design area, digital management of passive and active mixed area, and improvement of passive area. These strategies prevent direct isolation between the Monument and the environment while optimizing the indoor and outdoor wind environments.