Earthen-based building: In-situ drying kinetics and shrinkage

Touati, Karim; Guern, Michel Le; Mendili, Yassine El; Azil, Athmane; Streiff, François; Carfrae, Jim; Fox, Merritt B.; Goodhew, Steve; Boutouil, Mohamed

doi:10.1016/j.conbuildmat.2023.130544

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…Additionally, with its larger vegetal fiber content, the used soil is composed of kaolinite and illite with a high interfoliar space. These compounds can contain water molecules between their layers, resulting in high inter-crystalline swelling when submerged in water [19]. Figure 6 depicts the concentration of carbon dioxide (CO2) inside and outside the prototype building, along with external conditions.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Benzaama,

Touati,

El Mendili

et al. 2024

Energies

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The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments.

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Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Benzaama,

Touati,

El Mendili

et al. 2024

Energies

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“…The experimental results indicate that indoor air quality is marked by elevated relative humidity, possibly attributed to the continuous release of moisture from the cob construction into the indoor environment, even after 13 months from the last lift's implementation [34]. Additionally, the levels of carbon dioxide, volatile organic compounds, particulate matter, nitrogen dioxide, and ozone appear to be lower indoors than outdoors, although the impact of the damaged earthen floor and the dehumidifier's operation has been noted.…”

Section: • No 2 and Omentioning

confidence: 94%

Indoor Air Quality in Cob Buildings: In Situ Studies and Artificial Neural Network Modeling

Touati,

Benzaama,

El Mendili

et al. 2023

Buildings

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Knowledge of indoor air quality (IAQ) in cob buildings during the first few months following their delivery is of vital importance in preventing occupants’ health problems. The present research focuses on evaluating IAQ in cob buildings through a prototype built in Normandy, France. To achieve this, the prototype was equipped with a set of sensors to monitor various parameters that determine indoor and outdoor air quality. These parameters include relative humidity (RH), carbon dioxide (CO2), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM1 and PM10), and volatile organic compounds (VOCs). The obtained experimental results indicate that, overall, there is good indoor air quality in the prototype building. However, there are some noteworthy findings, including high indoor RH and occasional spikes in CO2, PM1, PM10, and VOCs concentrations. The high RH is believed to be a result of the ongoing drying process of the cob walls, while the peaks in pollutants are likely to be attributed to human presence and the earthen floor deterioration. To ensure consistent good air quality, this study recommends the use of a properly sized Controlled Mechanical Ventilation system. Additionally, this study explored IAQ in the cob building from a numerical perspective. A Long Short-Term Memory (LSTM) model was developed and trained to predict pollutant concentrations inside the building. A validation test was conducted on the CO2 concentration data collected on-site, and the results indicated that the LSTM model has accurately predicted the evolution of CO2 concentration within the prototype building over an extended period.

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“…Retrieved soil properties constituting cob mixture and classification can be found in Table 1. The mineralogical composition of soil 1 reveals the presence of the following major phases: quartz (54.8%), muscovite (26.2%), montmorillonite (6.9%), and albite (4.2%), with small occurrences of illite, kaolinite, goethite, rutile, and huntite (see Table 2) [18]. Subsequently, soil 1 is typical of silty soil.…”

Section: Soils Particle Size and Geotechnical Characterizationmentioning

confidence: 99%

Insight into the Optimization of Implementation Time in Cob Construction: Field Test and Compressive Strength Versus Drying Kinetics

Touati,

Al Sahmarany,

Le Guern

et al. 2023

Eng

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Mastering construction times is of paramount importance in making vernacular earth construction techniques attractive to modern clients. The work presented here is a contribution towards the optimization of the construction time of cob buildings. Therefore, this paper follows the evolution of a cob’s mechanical properties during its drying process in the case of a double-walling CobBauge system. Laboratory tests and in situ measurements were performed, and further results were described. Volumetric water content sensors were immersed in the walls of a CobBauge prototype building during its construction. The evolution of the cob layer’s compressive strength and Clegg Impact Value (CIV) as a function of its water content has been experimentally studied and discussed. These studies showed that compressive strength and CIV are correlated with water content, and both properties decrease exponentially with time. In this study, a new tool to evaluate cob’s mechanical performances in situ has been proposed, Clegg Impact Soil Tester. This was linked to compressive strength, and a linear relationship between these two properties was found. Finally, appropriate values of compressive strength and CIV to satisfy before formwork stripping and re-lifting were proposed. For this study’s conditions, these values are reached after approximately 27 days.

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Earthen-based building: In-situ drying kinetics and shrinkage

Cited by 12 publications

References 35 publications

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Indoor Air Quality in Cob Buildings: In Situ Studies and Artificial Neural Network Modeling

Insight into the Optimization of Implementation Time in Cob Construction: Field Test and Compressive Strength Versus Drying Kinetics

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