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Antico, F. C., Rojas, P., Briones, F., & Araya-Letelier, G. (2021). Animal fibers as water reservoirs for internal curing of mortars and their limits caused by fiber clustering. Constr. Build. Mater., 267, 120918.
Abstract: We present a bottom-up experimental research to address evidence of internal curing of mortars using randomly distributed pig-hair as water reservoirs. Plain and reinforced mortars with pig hair ranging from 0 to 8 kg of fibers per cubic meter of mortar were prepared. The microstructures of plain and reinforced mortars were scanned using electron microscopy and the microhardnesses were measured within
the bulk cement paste and cement paste near pig fibers. Electrical resistivity, surface absorption, and residual compressive strength of mortars after freeze-thaw cycles were used to test the effects of internal curing caused by pig hair. Natural fibers used to reinforce mortars increase their toughness and provide
part of the necessary water for internal curing, yet internal curing originated by the addition of natural fibers is not proportional to fiber dosage; where the potential to form fiber clusters increases as fiber dosage increases. Results show that there is an optimum fiber dosage that maximizes internal curing
caused by these fibers. This study contributes to the research on reinforced mortars with natural fibers to provide sustainable solutions for construction materials.
Keywords: Internal curing; Animal fiber; Reinforced mortar; Fiber clusters; Valorized waste; Macroscopic properties; Durability
Araya-Letelier, G., Antico, F. C., Burbano-Garcia, C., Concha-Riedeld, J., Norambuena-Contreras, J., Concha, J., et al. (2021). Experimental evaluation of adobe mixtures reinforced with jute fibers. Constr. Build. Mater., 276(2021), 122127.
Abstract: Due to their sustainability as well as physical and mechanical performance, different natural fibers, both vegetal and animal fibers, have been successfully used in adobe mixtures (AMs) to enhance properties such as cracking control, flexural toughness and water erosion resistance, among others. However, the use of jute fibers (JFs), one of the most largely produced vegetal fiber worldwide, has not been extensively studied on AMs. Consequently, this study evaluates the effects of the incorporation of varying dosages (0.5 and 2.0 wt%) and lengths (7, 15, and 30 mm) of JFs on the physical/thermal/mechanical/fracture and durability performance of AMs, a specific type of earth-based construction material widely used globally. Experimental results showed that the incorporation of 2.0 wt% dosages of JFs increased the capillary water absorption of AMs, which might affect AM durability. The latter result could be explained by the additional porosity generated by the spaces left between the JFs and the matrix of adobe, as well as the inherent water absorption of the JFs. The incorporation of JFs significantly improved the behavior of AMs in terms of thermal conductivity, drying shrinkage cracking control, flexural toughness and water erosion performance, without affecting their compressive and flexural strength. For example, flexural toughness indices were increased by 297% and crack density ratio as well as water erosion depth values were reduced by 93% and 62%, respectively, when 2.0 wt%-15 mm length JFs were incorporated into AM. Since the latter combination of JF dosage and length provided the overall best results among AMs, it is recommended by this study as JF-reinforcement scheme for AMs for construction applications such as adobe masonry and earth plasters.
Keywords: Jute fibers; Fiber-reinforced composites; Thermal conductivity; Mechanical characterization; Damage and durability assessment
Carrasco, M., Araya-Letelier, G., Velazquez, R., & Visconti, P. (2021). Image-Based Automated Width Measurement of Surface Cracking. Sensors, 21(22), 7534.
Abstract: The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.