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This paper introduces a numerical methodology for classifying and identifying types of bio-based materials through experimental thermal characterization. In contrast to prevailing approaches that primarily focus on thermal conductivity, our characterization methodology encompasses several thermal parameters. In this paper, the physical characteristics of seven types of bio-based concrete were analyzed, focusing on the thermal properties of palm- and esparto-fiber-reinforced concrete. The proposed method uses artificial intelligence techniques, specifically the k-means clustering approach, to segregate data into homogeneous groups with shared thermal characteristics. This enables the elucidation of insights and recommendations regarding the utilization of bio-based insulation in building applications. The results show that the k-means algorithm is able to efficiently classify the reference concrete (RC) with a performance of up to 71%. Additionally, the technique is more accurate when retaining only six centroids, which, among other things, allows all the characteristics associated with each type of concrete to be grouped and identified. Indeed, whether for k clusters k = 7 or k = 5, the technique was not able to predict the typical characteristics of 2% or 3% esparto concrete (EC). [ABSTRACT FROM AUTHOR]

In the early stages of building design, decisions are made about the building's form and envelope, but designers rarely base their decisions on sophisticated energy simulations, even though these features are critical to a building's energy performance. This paper employs three methods—empirical, parametric, and uncertainty—to assess the interconnectedness of building form, envelope, orientation, and occupancy regarding thermal comfort and energy consumption for heating and cooling a residential building across three regions: Gdyel (mediterranean climate), Oum El Bouaghi, and Constantine (semi-arid climate). The study variables include indoor air temperature, relative humidity, and energy consumption. The initial findings stem from an experiment conducted in an apartment on the top floor of a building in Gdyel, which allowed us to record the evolution of the variables mentioned throughout the year and validate the parametric results of the multi-zone model created in TRNSYS16 software. This study showed that for the considered climates, a compact form is more suitable; it was found that the top floor with SF = 0.57 needs about 30% to 54% more energy than the inter-floor with SF = 0.21. In addition, the heating and cooling methods and habits adopted by Algerian households are responsible for 18% to 35% on the top floor and the inter-floor, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We investigate the mechanical characterisation of mortars incorporating ferronickel slags (FNS) as partial substitution for cement. The studied slags come from the ferronickel production in New Caledonia. The microstructural characterisation shows that these slags are polycrystalline heterogeneous material mainly composed of forsterite, quartz and enstatite in addition to silica glass. Cement is replaced by up to 20 wt.% of ferronickel slags in the mortars. This upper level of substitution is achieved without compromising mortar's strengths. For all mixes, the maximum compressive and flexural strengths at all curing ages is obtained with a 5 wt.% replacement of cement by ferronickel slags. The larger effectiveness of FNS is attributed to its larger silica content, which increases the polymerisation degree of the silica chains in the calcium silicate hydrate (C-S-H) structure. Slag recycling reduces the storage time, waste management and environmental impact. It significantly lowers costs related to metallurgical processing. Slag substitution for cement, also lowers the carbon footprint. [ABSTRACT FROM AUTHOR]