Seasonality in Moisture Dynamics in the Walls of the rock-cut Churches in Lalibela, Ethiopia: Implications for Weathering

Moisture plays a key role in rock decay in the built and natural environments. Rock-cut sites are particularly vulnerable to moisture-related weathering as they are carved into rock outcrops and do not have impermeable foundations or roofs to retard the flow of moisture. To characterise the moisture dynamics and its influence on weathering of rock-cut sites, we undertook a moisture monitoring campaign using a non-destructive Microwave Moisture Measurement System (MMMS) at two monolithic rock-cut churches in Lalibela, Ethiopia. The results showed that the walls were more saturated at depth than on the surface during the wet season. This suggests that low surface temperature and higher moisture content at depth will lead to constant-rate drying and accumulation of salts on the surface of the walls during the wet season. In the dry season, there was higher saturation near the surface than at depth (falling-rate drying). High rock surface temperature during the dry season contributes to subsurface drying and accumulation of salts below the surface. This seasonally shifting moisture dynamics will lead to a complex and dynamic damage profile. This study highlights the significant wetting facilitated by a lack of impermeable roofs and foundations at rock-cut structures during rainy periods

BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification

Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully supervised learning requires manually fully annotated datasets which is costly and time-consuming. In this paper, we propose BERT-Flow-VAE (BFV), a Weakly-Supervised Multi-Label Text Classification (WSMLTC) model that reduces the need for full supervision. This new model (1) produces BERT sentence embeddings and calibrates them using a flow model, (2) generates an initial topic-document matrix by averaging results of a seeded sparse topic model and a textual entailment model which only require surface name of topics and 4-6 seed words per topic, and (3) adopts a VAE framework to reconstruct the embeddings under the guidance of the topic-document matrix. Finally, (4) it uses the means produced by the encoder model in the VAE architecture as predictions for MLTC. Experimental results on 6 multi-label datasets show that BFV can substantially outperform other baseline WSMLTC models in key metrics and achieve approximately 84% performance of a fully-supervised model.Comment: 8 pages, 4 figure

Priorities and Principles for Investment in Aquaculture Research by NSW Department of Primary Industries

This review examined the characteristics of the main aquaculture industries in NSW with respect to current impediments to growth, market development and future opportunities. Within this context, it examined the nature, funding and impacts of the NSW Department of Primary Industries’ current and proposed investments in aquaculture R&D and industry development, as well as its alignment with DPI and industry priorities.aquaculture, research evaluation, public good, Agribusiness, Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies, Q160,

Assessing wind-driven rain loads on traditional buildings using computational fluid dynamics and 3D digital documentation data

Moisture in building fabric from wind-driven rain (WDR) is associated with several erosion decay mechanisms. To relate the WDR load to known issues from moisture in traditional buildings, several methods can be used to calculate WDR loads on building facades. Computational Fluid Dynamics (CFD) enables detailed modelling of WDR for buildings by simulating coupled wind flow and rainfall, but implementations for traditional buildings often use simplified hard surface modelling of architectural details. We apply the open source OpenFOAM windDrivenRainFoam solver to 3D digital documentation survey data for Melrose Abbey, Scotland. This approach enables detailed representation of building geometry with flexibility to change resolution as required, based on an accurate high resolution geometric dataset

Measuring the impact of COVID-19 on heritage sites in the UK using social media data

The COVID-19 pandemic has had a profound impact on almost all aspects of society. Cultural heritage sites, which are deeply intertwined with the tourism industry, are no exception. The direct impacts of the virus on the population, as well as indirect impacts, such as government-mandated measures including social distancing, face coverings, and frequent temporary closures of sites, have greatly impacted visitor experiences at heritage sites. To quantitatively evaluate the impact of these measures from the perspective of visitors, we collected 1.4 millions visitor reviews from the Google Maps platform for 775 heritage sites. We analyzed visiting rates using the number of online reviews as a proxy and adopt state-of-the-art natural language processing techniques to more deeply understand visitor perception of preventive measures put in place to control the spread of COVID-19. Our findings reveal that even if visitor focus on COVID-19 has significantly decreased, there may still be notable difference between actual and expected number of reviews, suggesting that visitor involvement (e.g., number of visitors) for cultural heritage sites, especially urban indoor sites, needs more time to recover. Our findings further show that most comments by visitors to sites were associated with negative sentiment toward restricted access, but recognized the necessity of other safeguarding measures (e.g., social distancing and the requirement for face coverings). Moreover, they exhibited negative sentiment towards staff or other visitors who did not adhere to these measures. We make specific recommendations for heritage sites to adapt to the COVID-19 pandemic and a more general observation that the method used to gather information from online reviews in this paper will be effective in measuring visitor perceptions towards specific aspects of heritage sites, particularly in capturing changes in perception before and after unexpected or disruptive events at heritage sites

Preliminary Experimental Laboratory Methods to Analyse the Insulation Capacity of Vertical Greening on Temperature and Relative Humidity

Ground-based vertical greening is one of the well-known nature-based solutions that is widely used in city centres due to its small footprint and the large surface area of vegetation. Although the impact of vertical greening on the local microclimate has already been extensively researched, there is a poor understanding of the impact of vertical greening on historic building fabrics. The impact of vertical greening on microclimate environments has primarily been researched through in situ case-study monitoring; as such, there are currently no standard protocols for investigating this impact in laboratory studies. By performing simulations in controlled laboratory conditions, the influence of vertical greening on specific environmental conditions can be assessed as well as the significance for key mechanisms, such as the insulation capacity of a vegetation layer. Experimental results on the insulation capacity of vertical greening illustrate that the presence of vertical greening reduces the rate of heat exchange between the wall and the surrounding environment compared to the bare wall, resulting in a delayed temperature response of the wall. This delay varies across the seasons or its intensity, which is represented, for instance, by a more pronounced delay in the wall’s surface temperature response in summer than in winter. However, the magnitude of the insulation capacity is more pronounced in winter (up to +2.1 °C) compared to summertime. The insulation capacity of vertical greening is more likely to have a significant impact on façades with a lack of solar irradiation, such as façades facing north or shaded by built surroundings. This experimental investigation can help build an understanding of these processes more fundamentally and support the interpretation of in situ case-study monitoring as well as provide a standardized approach to investigate the environmental performance of vertical greening across climatic regions and seasons

Stone-built heritage as a proxy archive for long-term historical air quality: A study of weathering crusts on three generations of stone sculptures on Broad Street, Oxford

Black crusts on historic buildings are mainly known for their aesthetic and deteriorative impacts, yet they also can advance air pollution research. Past air pollutants accumulate in distinct layers of weathering crusts. Recent studies have used these crusts to reconstruct pollution to improve our understanding of its effects on stone-built heritage. However, the majority of the studies provide only coarse resolution reconstruction of pollution, able to distinguish between ‘inner = old’ and ‘outer = modern’ crust layers. In contrast, very few studies have linked distinct periods of exposure to pollution variations in the composition of these crusts. Here we address this research gap by developing a finer-scale resolution pollution record. Our study explored the unique configuration of limestone sculptures in central Oxford, which have been exposed over the last 350 years to three different periods of atmospheric pollution; the early Industrial Revolution, the Victorian period and the 20th century. When the first two generations of sculptures were moved to less polluted areas, their ‘pollution clocks’ were stopped. Here we discuss the potential of investigating the ‘pollution clock’ recorded in the geochemical makeup of each sculpture generation's weathering crust layers. We found the analysed crusts record clear changes related to the evolution of modes of transport and industrial and technological development in Oxford. Higher levels of Arsenic (As), Selenium (Se) are linked to pollution from coal burning during Victorian times and Lead (Pb) indicated leaded petrol use in modern times. Our work shows that stone-built heritage with a known history of air pollution exposure allows improving the pollution reconstruction resolution of these weathering crusts. The results provide the basis for calibrating long-term geochemical archives. This approach may be used to reconstruct past air quality and has the potential to inform stone weathering research and conservation, in addition to improving the reconstruction of historical pollution

Charge balance calculations for mixed salt systems applied to a large dataset from the built environment

Understanding salt mixtures in the built environment is crucial to evaluate damage phenomena. This contribution presents charge balance calculations applied to a dataset of 11412 samples taken from 338 sites, building materials showing signs of salt deterioration. Each sample includes ion concentrations of Na^{+}, K^{+}, Mg^{2+}, Ca^{2+}, Cl^{−}, NO_{3}^{−}, and SO_{4}^{2−} adjusted to reach charge balance for data evaluation. The calculation procedure follows two distinct pathways: i) an equal adjustment of all ions, ii) adjustments to the cations in sequence related to the solubility of the theoretical solids. The procedure applied to the dataset illustrates the quantification of salt mixture compositions and highlights the extent of adjustments applied in relation to the sample mass to aid interpretation. The data analysis allows the identification of theoretical carbonates that could influence the mixture behavior. Applying the charge balance calculations to the dataset validated common ions found in the built environment and the identification of three typical mixture compositions. Additionally, the data can be used as direct input for thermodynamic modeling

Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades

Crowdsourced images hold information could potentially be used to remotely monitor heritage sites, and reduce human and capital resources devoted to on-site inspections. This article proposes a combination of semantic image segmentation and photogrammetry to monitor changes in built heritage sites. In particular, this article focuses on segmenting potentially damaging plants from the surrounding stone masonry and other image elements. The method compares different backend models and two model architectures: (i) a one-stage model that segments seven classes within the image, and (ii) a two-stage model that uses the results from the first stage to refine a binary segmentation for the plant class. The final selected model can achieve an overall IoU of 66.9% for seven classes (54.6% for one-stage plant, 56.2% for two-stage plant). Further, the segmentation output is combined with photogrammetry to build a 3D segmented model to measure the area of biological growth. Lastly, the main findings from this paper are: (i) With the help of transfer learning and proper choice of model architecture, image segmentation can be easily applied to analyze crowdsourcing data. (ii) Photogrammetry can be combined with image segmentation to alleviate image distortions for monitoring purpose. (iii) Beyond the measurement of plant area, this method has the potential to be easily transferred into other tasks, such as monitoring cracks and erosion, or as a masking tool in the photogrammetry workflow