Evidence map›Paper›PMID 38204103›Full record

ArticleMaterials (Basel, Switzerland)2024

Study on the Preparation and Properties of Vegetation Lightweight Porous Concrete.

Qingyu Cao, Juncheng Zhou, Weiting Xu, Xiongzhou Yuan

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

4 authors.

Qingyu CaoMMC Group, Central Research Institute of Building and Construction, Beijing 10088, China.
Juncheng ZhouSchool of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Weiting XuSchool of Materials Science and Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0003-1549-7382
Xiongzhou YuanSchool of Traffic and Environment, Shenzhen Institute of Information Technology, Shenzhen 518172, China.

Funding

National Natural Science Foundation of China 51978678, 52178208National Science Fund for Distinguished Young Scholars of Beijing JQ22026The Fundamental Research Funds for the Central Universities 2023ZYGXZR031
6 · The paper itself

Abstract

The objective of this study is to formulate vegetated light porous concrete (VLPC) through the utilization of various cementing materials, the design of porosity, and the incorporation of mineral additives. Subsequently, the study aims to assess and analyze key properties, including the bulk density, permeability coefficient, mechanical characteristics, and alkalinity. The findings indicate a linear decrease in the volume weight of VLPC as the designed porosity increases. While higher design porosity elevates the permeability coefficient, the measured effective porosity closely aligns with the design values. The examined VLPC exhibits a peak compressive strength of 17.7 MPa and a maximum bending strength of 2.1 MPa after 28 days. Notably, an escalation in porosity corresponds to a decrease in both the compressive and the bending strength of VLPC. Introducing mineral additives, particularly silicon powder, is shown to be effective in enhancing the strength of VLPC. Furthermore, substituting slag sulfonate cement for ordinary cement significantly diminishes the alkalinity of VLPC, resulting in a pH below 8.5 at 28 days. Mineral additives also contribute to a reduction in the pH of concrete. Among them, silica fume, fly ash, fly ash + slag powder, and slag powder exhibit a progressively enhanced alkaline reduction effect.

Indexed as

alkalinitymechanical propertypermeability coefficientporosity

Identifiers

PMID38204103
PMCPMC10779460

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Registered trials

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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.