Machine learning models applied to moisture assessment in building materials

Dafico, Leticia C.M.; Barreira, Eva; Almeida, Ricardo M.S.F.; Vicente, Romeu

doi:10.1016/j.conbuildmat.2023.133330

Cited by 3 publications

(1 citation statement)

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“…Despite the wide variety of techniques available for calculating moisture content, measuring and monitoring moisture using a minimally destructive method, in real time, remains a challenge for both historical and modern buildings [23,24]. Although advanced imaging technologies and computational methods are widely used in building investigations, the determination of moisture content is still in need of further elaboration [25]. Additionally, climatic change, along with its accompanied phenomena, such as sea level rise, extreme weather conditions, floods, etc., constitutes a challenge for conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors

Karoglou,

Mouzakis,

Bakolas

et al. 2024

Applied Sciences

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Building materials’ moisture content defines the overall durability and serviceability of buildings and infrastructures. Conventionally, moisture content is estimated by weighing the materials before and after drying. In this work, the results of capacitance wired sensors for measuring moisture content were investigated. These sensors facilitate in situ measurement of capacitance values, with the aid of specialized installation equipment, and the results are accessible via a cloud-based software application. In practice, the main drawback associated with the application of these sensors is related to their calibration. Thus, it is important to investigate different algorithms which convert capacitance readings into moisture content values. To address this, various specimens covering a diverse set of building materials were prepared in the laboratory. This study focused on examining two bricks, two sedimentary stones with low and high porosity values. and four different kinds of mortars (with different binders, namely hydrated lime putty, cement, natural hydraulic lime, and hydrated lime powder and pozzolan). The results indicated that a linear model can be recommended for calibrating sensor capacitance to moisture content measurements. This model can be used for the prediction of building material moisture content with high accuracy, from saturated to the dry state, covering the full range of drying kinetics.

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Section: Introductionmentioning

confidence: 99%