Fungal growth in damp or water-damaged buildings worldwide is an increasing problem, which has adverse effects on both the occupants and the buildings. Air sampling alone in moldy buildings does not reveal the full diversity of fungal species growing on building materials. One aim of this study was to estimate the qualitative and quantitative diversity of fungi growing on damp or water-damaged building materials. Another was to determine if associations exist between the most commonly found fungal species and different types of materials. More than 5,300 surface samples were taken by means of V8 contact plates from materials with visible fungal growth. Fungal identifications and information on building material components were analyzed using multivariate statistic methods to determine associations between fungi and material components. The results confirmed that Penicillium chrysogenum and Aspergillus versicolor are the most common fungal species in water-damaged buildings. The results also showed Chaetomium spp., Acremonium spp., and Ulocladium spp. to be very common on damp building materials. Analyses show that associated mycobiotas exist on different building materials. Associations were found between (i) Acremonium spp., Penicillium chrysogenum, Stachybotrys spp., Ulocladium spp., and gypsum and wallpaper, (ii) Arthrinium phaeospermum, Aureobasidium pullulans, Cladosporium herbarum, Trichoderma spp., yeasts, and different types of wood and plywood, and (iii) Aspergillus fumigatus, Aspergillus melleus, Aspergillus niger, Aspergillus ochraceus, Chaetomium spp., Mucor racemosus, Mucor spinosus, and concrete and other floor-related materials. These results can be used to develop new and resistant building materials and relevant allergen extracts and to help focus research on relevant mycotoxins, microbial volatile organic compounds (MVOCs), and microparticles released into the indoor environment.

Associations between fungal species and water-damaged building materials.

There are several biological processes causing aging and damage to buildings. This is partly due to natural aging of materials and excessive moisture. The demands on durability, energy balance, and...

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Moisture and Bio-deterioration Risk of Building Materials and Structures

The aim of this study is to develop a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance of structural components of buildings right from the design stage. A review of the extant literature was carried out to identify factors that influence salvage performance of structural components of buildings during their useful life. Thereafter, a mathematical modelling approach was adopted to develop BWPE using the identified factors and principle/concept of Weibull reliability distribution for manufactured products. The model was implemented in Building Information Modelling (BIM) environment and it was tested using case study design. Accordingly, the whole-life salvage performance profiles of the case study building were generated. The results show that building design with steel structure, demountable connections, and prefabricated assemblies produce recoverable materials that are mostly reusable. The study reveals that BWPE is an objective means for determining how much of recoverable materials from buildings are reusable and recyclable at the end of its useful life. BWPE will therefore provide a decision support mechanism for the architects and designers to analyse the implication of designs decision on the salvage performance of buildings over time. It will also be useful to the demolition engineers and consultants to generate pre-demolition audit when the building gets to end of its life.

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Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator

Review of mould prediction models and their influence on mould risk evaluation

Laboratory study to determine the critical moisture level for mould growth on building materials

Numerical simulation of mold growth can be used as one of the hygrothermal performance criteria of building structures. Mold growth is one of the first signs of too-high moisture content of materials, and it may affect the indoor air quality and also the appearance of the visible surfaces. Mold growth potential can be predicted by solving a numerical value, mold index, by using the dynamic temperature and relative humidity histories of the subjected material surfaces. The model was originally based on mold growth of wooden materials, but it has now been completed with several other building materials. The model can be used parallel with heat, air, and moisture simulation models or as a post-processing tool. This paper presents the latest findings of mold growth and the modeling of these factors on different materials. The mold growth model has been improved by taking into account the effect of seasonal, long dry or cold periods that do not allow growth. This includes mechanisms for the decrease of mold level (decline of mold index) during unfavorable growth periods and the intensity of the growth after these periods. The laboratory and field results show that the sensitivity of the mold index level may vary in a large range depending on materials. Also, the performance on the interface of two materials has been studied. Instead of modeling the performance separately for each material or product, the materials are presented as different mold sensitivity classes varying from resistant to very sensitive. The sensitive class corresponds to the performance of pine sapwood, which was one basic material in the original model format. Other materials are presented by using the detected correlations between these materials. The mold growth sensitivity classes, decline of the growth level, comparison to detected mold level in materials, and numerical application in practical hygrothermal performance analysis are presented and discussed. MOTIVATION AND OBJECTIVES FOR FURTHER DEVELOPMENT OF THE MOLD GROWTH MODEL Numerical simulation of heat, air, and moisture performance of building structures generates the prediction of hygrothermal conditions in different parts of the analyzed structure. Also, monitoring of laboratory experiments and site investigations produces large amounts of data from critical parts of structures. This data should be post processed in order to evaluate the risks connected to overall performance, service life, interaction with indoor climate conditions, and structural safety. Mold growth is one of the first signs of biological deterioration caused by excess moisture; therefore, mold growth can be used as one of the best hygrothermal performance criteria of building structures. Mold does not deteriorate the material, but it is a sign of too-high moisture content and it represents a risk for other moisture-caused problems, such as decay. Mold affects the appearance of the surface and can severely affect the indoor air quality when the growth is in contact with indoor air or with the leakage air flowing into the room space. The mathematical model of mold growth was developed by Hukka and Viitanen (1999) based on regression analysis of the measured data (Viitanen 1996; Viitanen and Ritschkoff 1991) for calculating the development of mold growth, which is expressed as the mold index. An index value from 0 to 6 is defined to describe the evaluation of mold growth on a surface © 2010 ASHRAE. Tuomo Ojanen is a senior research scientist and team leader at VTT Expert Services Ltd, Finland. Hannu Viitanen is a senior research scientist and team leader at VTT Bioprocessing, Finland. Ruut Peuhkuri worked as a research scientist at VTT during the research and is currently a senior consultant with Passivhus.dk ApS, Næstved, Finland. Kimmo Lähdesmäki and Kati Salminen are research scientists and Juha Vinha is a docent in the Department of Civil Engineering at Tampere University of Technology, Tampere, Finland. a) on a microsopic level (1– 2) and b) when the growth can be detected visually (3–6). This mold index is based on the detectable growth of different mixed mold species. The first version of this model was based on a great number of measurements on pine and spruce sapwood material. This model has been used to analyze (in parallel or in post processing) the result derived from numerical simulation models for the dynamic temperature and relative humidity histories of the critical material surfaces. The mold growth risk analysis based on sensitive wooden materials has been applied also for different material layers that have soiled, dusty surfaces and those surfaces having contact with wood-based materials. Since the first version of the model, the research has included several experimental studies on conditions for mold growth, primarily on wood but also on other building materials. In order to predict the risks of mold growth in varying types of structures made of several building products and materials, it is obvious that an improved model to cover several typical building materials has to be developed. RESEARCH CARRIED OUT TO IMPROVE THE MODEL A three-year research project was carried out at Technical Research Center of Finland (VTT) and Tampere University of Technology. This project included large sets of steady-state and dynamic laboratory experiments for common building materials (Salminen et al. 2009), monitoring of mold growth in material surfaces and structures under real climate conditions, and long-period climate chamber experiments. The results of these findings were used to improve the existing numerical model for mold growth. This paper presents the development of the mold growth model in this project (Peuhkuri et al. 2009; Ojanen et al. 2009), which items were taken into account and how these parameters were studied, and the results interpreted numerically for different materials and conditions. The experiments and their findings are presented and discussed only from the modeling aspect. These results are presented in a concise way, and the main findings are shown as the improvements of the numerical model. The emphasis is on the comparison between experiments and the outcome of the new modeling principles. MATERIALS USED IN THE RESEARCH Some typical building materials were chosen for the experiments: spruce board (with glued edges), concrete (K30, maximum grain size 8 mm), aerated concrete, cellular concrete, polyurethane thermal insulation (PUR, with paper surface and with polished surface), glass wool, polyester wool, and expanded polystyrene (EPS). Pine sapwood was used as a reference material. This set of products cannot entirely represent all the products in the building material group, but it gives improved approximation on the mold growth sensitivity of each. The following results are based on the controlled laboratory and well-monitored site experiments of the chosen materials and structures where these products were used. UPDATING THE EXISTING NUMERICAL MOLD GROWTH MODEL The mold growth model based on experiments with wood was updated to be valid also for the mold growth prediction of other building materials. The idea in this research was to keep the original model structure and to adapt the mold growth parameter values of different materials to the existing model. Some improvements were applied for the model structure to better adjust different growth phenomena. The following sections represent the modeling principles for different mold growth parameters. MOLD GROWTH LEVEL—MOLD INDEX Determination of the mold growth levels is the fundamental element of the whole simulation of this biological phenomenon. This determination sets an interpretation of the visual growth levels as numerical values. This is needed both in the evaluation of the experimental results and in the assessment of the simulation results. Figure 1 represents how mold growth was studied under constant conditions for this research. Closed containers had saturated salt solution vessels to maintain known constant humidity levels. There were nine test samples of each material used in the tests. Some focusing was done to better take into account the different mold growth types with different materials and surfaces. The main difference compared to the version for wood-based materials was in the area that is not visible to the naked eye. It was found out that with some materials the mold growth coverage could be quite high already in microscopic areas (see Figure 2). Therefore, the mold index Figure 1 Laboratory test setup with small samples; there were nine samples of each material.

Mold growth modeling of building structures using sensitivity classes of materials

There are several biological processes causing aging and damage to buildings. Partly this is due to natural ageing of materials, and partly it is caused by excess moisture. The demands on durability, energy balance and health of houses are continually rising. For mould development, the minimum (critical) ambient humidity requirement is shown to be between RH 80 and 95 % depending on other factors like ambient temperature, exposure time, the type and surface conditions of building materials. For decay development, the critical humidity is above RH 95 %. Mould typically affects the quality of the adjacent air space with volatile compounds and spores. Decay development forms a serious risk for structural strength depending on moisture content, materials, temperature and time. The worst decay damage cases in Finland are found in the floors and lower parts of walls, where water accumulates due to different reasons. Modelling of mould growth and decay development based on humidity, temperature, exposure time and material will give new tools for the evaluation of durability of different building materials and structures. The models make it possible to analyse the critical conditions needed for the start of biological growth, but it is also a tool to measure the progress of mould and decay development under different conditions. Numerical simulation makes it possible to evaluate the risk and development of mould growth on the structure surfaces. Thus the moisture capacity and moisture transport properties in the material and at the surface layer have to be taken into account in the simulations. In practice there are even more parameters affecting mould growth, e.g. thickness of the material layers combined with the local surface heat and mass transfer coefficients. Therefore, the outcome of the simulations and in-situ observations of biological deterioration may not agree. In the present paper, results on mould growth in different materials and wall assemblies will be shown and models on the risk of mould growth and decay development will be evaluated. 1 H. Viitanen, T. Ojanen, R. Peuhkuri and L. Paajanen are senior research scientists, VTT, Technical Research Centre of Finland, Espoo, Finland 2 J. Vinha is senior assistant and senior research scientist, K. Salminen is research scientist and K. Lähdesmäki is assistant research scientist, Faculty of Built Environment, Department of Civil Engineering, Tampere University of Technology, Tampere, Finland.

Kimmo Lähdesmäki

Papers

Moisture and Bio-deterioration Risk of Building Materials and Structures

Mold growth modeling of building structures using sensitivity classes of materials

Moisture and biodeterioration risk of building materials and structures

Classification of material sensitivity: New approach for mould growth modeling

Mould growth modeling of building structures using sensitivity classes of materials