In this ongoing research project, advanced drone technology is employed in conjunction with simula-tion models to assess the impacts of urban climate, with a specific focus on Urban Heat Islands (UHI). The novel methodology was tested in the historical downtown areas of two German cities. Through the creation of high-resolution 3D-models of urban structures and the analysis of material properties, the research project aims to accurately predict the cli-mate impacts of urban planning decisions.

The results of the project provide planners with pre-cise insights for evaluating adaptation measures and mitigating the adverse effects of climate change in cities, ultimately improving living conditions there-in. The research undertaking also pioneers the use of high-temporal-resolution drone data for analyzing temperature patterns and considers various contribu-tions to the heat island effect for comprehensive climate-adaptive urban planning.

3D Concrete Printing (3DCP) is an emerging field that utilizes existing Fused Deposition Modeling (FDM) technologies to automate the building construction process. There are many similarities between conventional FDM technologies and 3DCP technologies, such as hardware design and software workflow, but there are also many differences. Due to the use of concrete as the printing material, the 3D concrete printing workflow cannot adopt the FDM printing workflow without modifications. This research investigated a new integrated system for the 3DCP process to improve the reliability of the 3DCP process. The system includes a slicer module, a path verification module, a g-code generation module, and a scaled physical printing procedure. A pilot study showed that this integrated system can screen certain human-made errors and improve the success rate of 3D concrete printing tasks.

This manuscript presents a validated approach for the abstraction of building glazing for analytical building performance assessment. The proposed dynamic analytical calculation uses a series of pre-generated adjustment coefficients, which are correction factors based on the solar angle of incidence (AOI), for calibrating a given window’s solar heat gain coefficient (SHGC). The goal is to achieve consistency in predicting solar gains through glazings between the different building performance assessment methodologies ranging from early-stage design methods to full-building detailed simulation.

Key words: analytical analysis; solar gain through windows;

We propose a non-probabilistic method for propagating uncertainties represented as intervals through a building performance simulation. The method only requires a single simulation without any prior computation. It is based on Taylor model arithmetic, and solves previous issues with interval-based methods such as the dependency problem by using truncated Taylor polynomials. As a result, the method provides an approximate estimate of uncertainty. However, initial results compare favorably in accuracy to Monte Carlo. Results are shown for a cubic function with 10,000 uncertain variables, and transient thermal simulations of a box with static and dynamic external boundary conditions. The proposed method is promising for efficient non-probabilistic uncertainty quantification in building performance simulation and warrants further investigation and refinement.