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Record W4411102203 · doi:10.5267/j.esm.2025.3.003

Experimental investigation of insulated mortar for building envelope systems

2025· article· en· W4411102203 on OpenAlexvenueno aff
Amer Matrood Imran, Mohammed Alhwayzee, Farhan Lafta Rashid, Borhan Beigzadeh

Bibliographic record

VenueEngineering Solid Mechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMortarEnvelope (radar)Materials scienceBuilding envelopeStructural engineeringComposite materialMechanical engineeringEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents an experimental study to investigate some of the thermal and strength behaviors of a new mortar material which was prepared by adding some of the residues of agricultural Iraqi biomass materials such as wood sawdust, reed, corn cobs, and their blending. These biomass materials are available in plenty of amounts in Iraq / Karbala City. These materials are blended with sand and cement, which are raw materials for mortar preparation, in different percentages to produce new types of mortar. The major focal area of interest is to identify the likelihood of applying these products as external wall insulating material to minimize heat transfer from outside to a building. Thermal conductivity, water absorption and skeletal density, and compressive strength at 7 and 28 days of the new mortars was also determined in the work. Comparing the performances obtained it was found that the new mortar containing wood sawdust had the highest compressive strength values While the best improvement in heat insulation was recorded in the mortar containing corn cobs compared to the other types. The results presented here prove that this mortar can be recommended for building purposes, specifically for exterior wall cladding. It provides good thermal resistance and improves the fortification of building walls; it also affords an added benefit of being cheaper and therefore fashionable for construction related uses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.217
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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