Evidence map›Paper›PMID 40271659›Full record

ArticleMaterials (Basel, Switzerland)2025

The Bearing Characteristics of a Grillage Root Foundation Based on a Transparent Soil Material: Enhancing the Bearing Capacity.

Zehui Ma, Junjie Wang, Xuefeng Huang, Zhifeng Ren, Hao Wang

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zehui MaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Junjie WangSchool of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Xuefeng HuangSchool of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Zhifeng RenCollege of Architecture and Energy Engineering, Wenzhou University of Technology, Wenzhou 325000, China.
Hao WangChina Merchants Zhongyu Engineering Consulting (Chongqing) Co., Ltd., Chongqing 400067, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The construction of a power grillage is of great significance for promoting local economic development. Identifying the characteristics of foundation damage is a prerequisite for ensuring the normal service of the power grillage. To investigate the bearing mechanism and failure mode of the grillage root foundations, a novel research method with a transparent soil material was used to conduct model tests on different types of foundations using particle image velocimetry (PIV) technology. The results indicate that, compared to traditional foundations, the uplift and horizontal bearing capacities of grillage root foundations increased by 34.35% to 38.89% and by 10.76% to 14.29%, respectively. Furthermore, increasing the base plate size and burial depth can further enhance the extent of the soil displacement field. Additionally, PIV analysis revealed that the roots improve pile-soil interactions, transferring the load to the surrounding undisturbed soil and creating a parabolic displacement field during the uplift process, which significantly suppresses foundation displacement. Lastly, based on experimental data, an Elman neural network was employed to construct a load-bearing capacity prediction model, which was optimized using genetic algorithms (GAs) and the whale optimization algorithm (WOA), maintaining a prediction error within 3%. This research demonstrates that root arrangement enhances the bearing capacity and stability of foundations, while optimized neural networks can accurately predict the bearing capacity of grillage root foundations, thus broadening the application scope of transparent soil materials and offering novel insights into the application of artificial intelligence technology in geotechnical engineering. For stakeholders in the bearing manufacturing industry, this study provides important insights on how to improve load-bearing capacity and stability through the optimization of the basic design, which can help reduce material costs and construction challenges, and enhance the reliability of power grillage infrastructure.

Indexed as

Elman neural networkmachine learning optimizationmodel testingpile–soil interactionprediction of bearing capacityroottransmission tower foundationtransparent soil

Identifiers

PMID40271659
PMCPMC11989634

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.