Thermal conductivity characteristics and predictive modeling of rubber sand composite geomaterials under multiple influencing factors

Waste tire rubber has low density, high elasticity, and excellent fatigue resistance. When mixed with sand, it forms a sustainable composite geomaterial in which thermal conductivity (TC) governs thermal performance. This study systematically investigated the TC of rubber-sand composite geomaterials (RSCG) with varying rubber particle sizes, rubber contents, water contents, and dry densities using the transient thermal probe method. Scanning electron microscopy (SEM) was also used to examine heat-transfer pathways and thermal-resistance mechanisms. Results show that TC decreases rapidly at low rubber contents and then more gradually with further rubber addition, whereas TC increases monotonically with dry density. Water content generally increases TC, but its effect weakens at higher rubber contents. Fine rubber particles (40 mesh) more effectively fill pores and reduce direct sand-sand contacts, causing a pronounced TC decrease at relatively low rubber contents. In contrast, coarse rubber particles (20 mesh) form more heterogeneous pore structures, allowing water-induced TC enhancement to remain pronounced over a broader rubber-content range. Where an identifiable turning point was observed, the critical water contents for both particle-size systems were mainly 8%-9%. A Wiener-bounds-based four-phase TC model considering rubber, river sand, water, and air was developed and showed significantly better predictive accuracy than the Kersten and Gangadhara models. The findings support TC prediction, mixture optimization, and engineering applications of RSCG.

Authors

Institutions

Publication Details

Journal
Thermal Science and Engineering Progress
Published
2026-10-14
DOI
https://doi.org/10.1016/j.tsep.2026.104961
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Thermal conductivity characteristics and predictive modeling of rubber sand composite geomaterials under multiple influencing factors

Caijin Wang, Xianming Hou, Honglin Ren, Wenliang Wang et al.
Thermal Science and Engineering Progress
Soil and Unsaturated Flow
article

Thermal conductivity characteristics and predictive modeling of rubber sand composite geomaterials under multiple influencing factors

Caijin Wang, Xianming Hou, Honglin Ren, Wenliang Wang, Zhiming Liu, Liangfu Xie, Tao Zhang, Xuejun Liu, Bo Yang, Yanjun Li, Qiao Li, Hongjian Zhang
article en

Abstract

Waste tire rubber has low density, high elasticity, and excellent fatigue resistance. When mixed with sand, it forms a sustainable composite geomaterial in which thermal conductivity (TC) governs thermal performance. This study systematically investigated the TC of rubber-sand composite geomaterials (RSCG) with varying rubber particle sizes, rubber contents, water contents, and dry densities using the transient thermal probe method. Scanning electron microscopy (SEM) was also used to examine heat-transfer pathways and thermal-resistance mechanisms. Results show that TC decreases rapidly at low rubber contents and then more gradually with further rubber addition, whereas TC increases monotonically with dry density. Water content generally increases TC, but its effect weakens at higher rubber contents. Fine rubber particles (40 mesh) more effectively fill pores and reduce direct sand-sand contacts, causing a pronounced TC decrease at relatively low rubber contents. In contrast, coarse rubber particles (20 mesh) form more heterogeneous pore structures, allowing water-induced TC enhancement to remain pronounced over a broader rubber-content range. Where an identifiable turning point was observed, the critical water contents for both particle-size systems were mainly 8%-9%. A Wiener-bounds-based four-phase TC model considering rubber, river sand, water, and air was developed and showed significantly better predictive accuracy than the Kersten and Gangadhara models. The findings support TC prediction, mixture optimization, and engineering applications of RSCG.

Thermal Science and Engineering ProgressVol. 79
Nanjing Tech University (CN), Xinjiang Institute of Engineering (CN), Southeast University (CN), Xinjiang University (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Soil and Unsaturated Flow
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.