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People & Buildings

Masters Conference 2024

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MC2024

Masters Conference 2023

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MC2023

Session 1: Various Themes on People and Buildings

Correlation analysis of health and mould in buildings: based on the Health Survey for England

Shiqi Yang and Hector Altamirano

Mould in buildings is a significant public health issue in England. It affects the living environment of residents and may also have some negative health effects. This study uses the Health Survey for England (HSE) 2010 dataset to explore the correlation between building mould and health data, and the relationship between mould and building conditions, socio-economics, and lifestyle habits. This paper summarises 23 potentially mould-relevant features based on the literature review and database. It also redefines some features, such as the housing overcrowding criterion. The study used multiple logistic regression to explore the correlations between these key characteristics. The study results show significant correlations between housing crowding, resident education levels, income levels, pet ownership, housing tenure and regional differences and building mould. There were also correlations between mould and respiratory health conditions and mental health issues such as sleep difficulties. Research into the relationship between mould and health in dwellings could be improved by adding more mould-related questions to future HSE surveys.  

Keywords: Mould growth; Health; Mould in buildings; Building feature; Moisture.

– Theme: Health and mould in buildings –


What is the Effect of Ramadan on Domestic Occupancy Patterns and Energy-Use in Muslim Households in London Compared to Pre-Ramadan

Hanaa Yakoub

Ramadan, the ninth month of the Islamic Hijri calendar, involves fasting and other forms of worship. While behaviour changes during Ramadan are well-documented, there’s limited research linking these changes to domestic energy use. This study aims to explore the reasons for changes in domestic behaviours and their impact on energy use during Ramadan. Seven participants were studied using a mixed-methods approach, including interviews, occupancy sensors, and energy data, to examine changes during Ramadan (March-April 2023-2024). Interviews revealed significant shifts in routines, such as increased nighttime activity, altered sleep patterns, and more intense cooking and socialising. Occupancy sensors showed peaks in kitchen activity around mealtimes, and energy data indicated more peaks in electricity use. These findings highlight the need for culturally sensitive energy management strategies to reduce peak loads and promote energy-saving behaviours, especially given the current energy and climate crises and fuel poverty affecting many Muslims in the UK.  

Keywords: Ramadan; Occupancy; Behaviour Pattern; Energy-use; Demand-side management. 

– Theme: Assessing occupants wellbeing and comfort –


Evaluating Large Language Models (LLMs)’ Understanding of ‘Black Crusts’ Predictive Models for Built Heritage Preservation

Wenrui Sun, Dr. Josep Grau-Bove, Dr. Daniela Reggio

This study evaluates the understanding of LLMs in predicting limestone sulphation, described in common language as ‘black crusts’, which is an environmental decay damaging for historic buildings, implying gypsum formation on the surface of carbonatic materials.

The research question is: ‘To what extent can built heritage managers use LLMs for preservation advice?’. GPT-4, Claude, and OpenArt were used for evaluation, and prompts were designed to test different aspects of limestone sulphation predictions. Gypsum thickness calculated at various time intervals from published studies was prompted to the LLMs to generate new predictions. These results were then cross-referenced with predictive models to assess accuracy. 

The findings indicate that LLMs produced varying results each time, with significant discrepancies compared to published models. Numerical predictions, data fitting, and image forecasting based on LLMs were explored, underscoring the limitations of LLMs in predictive modelling. Further testing is required to leverage LLMs capabilities in heritage preservation. 

Keywords: Limestone Sulphation; LLMs; Black Crusts; Heritage Preservation.

– Theme: Heritage Preservation –


How much do Large Language Models know about panel paintings preservation?

Fengjun Li, Dr. Daniela Reggio and Dr. Josep Grau-Bove

This study evaluates the capability of large language models (LLMs) in understanding the preservation of paintings on panel by comparison with the predictions obtained through the digital platform HERIe. The latter is specialized tool for heritage object risk assessment. Four large language models (LLMs) – ChatGPT 3.5, ChatGPT 4, Claude, and Gemini – were tested asking what are the levels of strain experienced by panel paintings under different conditions. The models were also tested on their ability to rank different environments conditions in order of suitability for storing panel paintings and were examined whether the languages of prompts affected results. The study concludes that while LLMs demonstrate a general understanding of wood panel preservation principles, they lack the specialized calculation abilities of purpose-built tools like HERIe for precise risk assessment in cultural heritage preservation. 

Keywords: panel paintings; preservation; microclimate; LLMs. 

– Theme: Building microclimate and heritage preservation –