Enhancing Self-Sensing Properties of Cement–Soil Enabled by the Synergy of Waste Carbon Black and Steel Slag

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Authors: Jinkang Lian, Shaofei Jiang, Chang Xia, Pengze Li, Xueya Le, Zeyu Chen
Cited by: 0
References: 31

Abstract

This study develops a novel self-sensing cement-soil composite (SSCS) that functions as an integrated sensor for structural health monitoring of ground improvement works. By incorporating industrial by-products (waste carbon black (CB) and steel slag (SS)), the composite achieves a synergistic enhancement of mechanical, electrical, and self-sensing properties. The mix proportions were optimized through an orthogonal experimental design, which investigated the effects of pretreated CB, SS, and cement on the fluidity, unconfined compressive strength (UCS), and electrical resistivity (ER). Range and variance analyses identified the influential factors, and the optimal mix was determined using an efficacy coefficient method. The microstructure and self-sensing were analyzed. The results indicate that SS enhanced fluidity, density, and UCS (by 83.5 %), while CB reduced ER by 2-3 orders of magnitude. Furthermore, a high-accuracy prediction model (with an R2>0.977) was established for property estimation, and the optimized mixture showed a deviation of less than 9.9% in key parameters. Microstructural analyses revealed that the combined use of CB and SS decreased harmful porosity, with SS promoting secondary hydration reactions and enhancing the bonding between CB and the cementitious matrix. The SSCS enables real-time stress monitoring with a high stress sensitivity of up to 24.0. The findings demonstrate that the synergistic use of CB and SS not only improves the mechanical, electrical, and self-sensing properties of cement-soil, but also promotes the sustainable utilization of industrial by-products, offering a novel and practical solution for intelligent foundation improvement and monitoring.

Fields of Study & Subjects

Smart Materials for Construction Geotechnical Engineering and Soil Stabilization Materials Engineering and Processing

Publication details

Journal / source
IEEE Sensors Journal
Volume
26
Issue
5
Pages
6587–6596
Publication date
27 January 2026