Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials.

Zhu Shi, Wenyao Peng, Chaoqun Xiang, Liang Li, Qibin Xie

Open access · goldAbstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

1.1field-weighted citation impact, top 27% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 7 citations in OpenAlex.

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 at 3 institutions in 2 countries.

Zhu ShiHunan Road and Bridge Construction Group Co., Ltd., Changsha 410075, China.

Wenyao PengHunan Pingyi Expressway Construction and Development Co., Ltd., Changsha 410004, China.

Chaoqun XiangHunan Road and Bridge Construction Group Co., Ltd., Changsha 410075, China.

Liang LiDepartment of Civil Engineering, Central South University, Changsha 410075, China.

Qibin XieDepartment of Civil Engineering, Central South University, Changsha 410075, China.

Central South University · CNShanghai Construction Group (China) · CNConstruction Development (Qatar) · QA

Funding

Hunan Transportation Science and Technology Foundation 202017National Natural Science Foundation of China 51908558

6 · The paper itself

Abstract

Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2

Indexed as

asphalt mixturecement concreteeffective thermal conductivityhomogenizationneural network

Identifiers

PMID37176204

DOI10.3390/ma16093322

PMCPMC10179405

OpenAlexW4366814652

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.