Utilising construction and demolition waste in soft soil stabilisation: A prediction model for enhanced strength and stiffness

IF 4.9 2区工程技术 Q1 ENGINEERING, CIVIL

Transportation Geotechnics Pub Date : 2025-03-01 DOI:10.1016/j.trgeo.2025.101530

Ecem Nur Barisoglu , Taher Ghalandari , Diederik Snoeck , Ramiro Daniel Verástegui-Flores , Gemmina Di Emidio

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引用次数: 0

Abstract

Utilising recycled materials, such as construction and demolition waste (C&DW), into soil improvement projects offers a promising solution to reduce the environmental impact of the C&DW industry. This approach helps address issues related to waste generation, resource depletion, and environmental degradation, while enhancing the overall sustainability and resilience of soil stabilisation efforts. This study investigates the effectiveness of incorporating recycled C&DW into cement-treated peat and clayey soils to enhance their strength and stiffness. To achieve this goal, laboratory experiments were conducted on over 296 soil specimens to assess their Unconfined Compressive Strength (UCS), small-strain Young’s modulus (E₀) and shear modulus (G₀). These tests included varying curing times (28, 60, 90, and 120 days), different cement and recycled material content, and water-to-cement ratios. Moreover, laboratory testing methods for determining geotechnical parameters are often time-consuming and prone to challenges. In this context, reliable predictive models, such as artificial neural networks (ANNs), offer an efficient alternative for accurately assessing these parameters.

The findings of this research reveal that, along with cement content, the water-to-cement ratio (w/c) and curing time are key factors influencing the strength and stiffness of treated soft soils, underscoring their critical role in soil stabilisation. Additionally, while minimizing cement content and increasing RM yield improvements in both peat and clay, the effect is more pronounced in peat due to the time-dependent nature of pozzolanic reactions. This suggests that achieving optimal performance with increased strength and stiffness requires a carefully balanced RM content. Finally, the study demonstrates that ANN-based models can accurately predict the strength and mechanical properties of soft soils, offering a viable alternative to traditional UCS and FFR tests.

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来源期刊

Transportation Geotechnics Social Sciences-Transportation

CiteScore

8.10

自引率

11.30%

发文量

194

审稿时长

51 days

期刊介绍： Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.