ArticleMaterials (Basel, Switzerland)2022
Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Understanding the mental health effects of social support among university students: a hybrid SEM-ANN approach.Scientific reports · 2026Article
- Determinants of culinary Students' Entrepreneurial Intentions: The role of social media use within dual-theory frameworks.PloS one · 2026Article
- Modeling older adults' continuance intention toward mobile health apps: a dual-path SEM-ANN approach.BMC public health · 2025Article
- Stabilization of Expansive Soils Using Cement-Zeolite Mixtures: Experimental Study and Lasso Modeling.Materials (Basel, Switzerland) · 2025Article
- Experimental analysis of the cyclic behavior of rammed earth walls reinforced with arundo donax natural fiber.Heliyon · 2024Article
- Experiment and Analysis of Variance for Stabilizing Fine-Grained Soils with Cement and Sawdust Ash as Liner Materials.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Earth materials have been used in construction as safe, healthy and environmentally sustainable. It is often challenging to develop an optimum soil mix because of the significant variations in soil properties from one soil to another. The current study analyzed the soil properties, including the grain size distribution, Atterberg limits, compaction characteristics, etc., using multilinear regression (MLR) and artificial neural networks (ANN). Data collected from previous studies (i.e., 488 cases) for stabilized (with either cement or lime) and unstabilized soils were considered and analyzed. Missing data were estimated by correlations reported in previous studies. Then, different ANNs were designed (trained and validated) using Levenberg-Marquardt (L-M) algorithms. Using the MLR, several models were developed to estimate the compressive strength of both unstabilized and stabilized soils with a Pearson Coefficient of Correlation (R
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Registered trials
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