Thermo-mechanical characterization and numerical modeling of lightweight mortars incorporating natural Pozzolan and expanded clay
Source abstract
Abstract
This research studies the thermo-mechanical properties of lightweight mortars (LWM) based on natural pozzolan (NP) from the Moroccan Middle Atlas, which is a porous volcanic rock formed by rapid cooling during explosive eruptions, and expanded clay aggregates (EC). The purpose is to find optimal mortar mixtures for better thermal insulation with reasonable mechanical properties for green building construction. Experimental investigation includes density, specific heat, thermal conductivity, compressive strength, tensile strength, and elastic modulus. In addition, an SPH implicit numerical model simulates thermo-mechanical behavior and validates experimentally derived results. SPH was employed due to its mesh-free and Lagrangian nature, which provides significant advantages over traditional methods, particularly when handling large deformations, porous materials, and complicated geometries without the requirement of meshing or remeshing. Results show that high EC content is responsible for significantly reducing thermal conductivity with respect to performance improvement as an insulation material while reducing compressive and tensile strengths as well as increased porosity. Alternatively, NP-based mortars exhibit improved mechanical strength in terms of higher thermal conductivity. Numerical simulations are in good agreement with experiments and validate the applicability of the SPH method for simulating heat transfer in light mortars. The result highlights the thermal vs. mechanical reliability trade-off, necessitating optimized mortar composition for the development of energy-efficient and structurally strong building material.
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Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Innovations in Concrete and Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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