<p>The grid hard crust (GHC), an innovative artificial structure formed by in situ solidification, comprises an upper hard crust layer and lower cement soil walls. Compared with the conventional single-thickness hard crust (STHC), the GHC achieves a lower total cement mass <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(m_c\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>m</mi> <mi>c</mi> </msub> </math></EquationSource> </InlineEquation> while maintaining comparable settlement control. However, its application has been limited by the lack of a comprehensive design theory. This study develops an analytical model for predicting GHC deformation by integrating Timoshenko foundation beam theory, the structural displacement method, and nonlimit-state earth pressure theory. The model computes four deformation parameters: the maximum vertical displacement <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(y_{\text {max}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>y</mi> <mtext>max</mtext> </msub> </math></EquationSource> </InlineEquation>, the differential settlement <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\Delta _{\text {max}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi mathvariant="normal">Δ</mi> <mtext>max</mtext> </msub> </math></EquationSource> </InlineEquation>, the horizontal displacement <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(x_{\text {max}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>x</mi> <mtext>max</mtext> </msub> </math></EquationSource> </InlineEquation>, and the stress efficiency <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\eta _{\text {load}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>η</mi> <mtext>load</mtext> </msub> </math></EquationSource> </InlineEquation>. The proposed model is validated against PLAXIS 2D simulations and field tests, showing good agreement. A parametric study further identifies key influencing factors and establishes preliminary design ranges for critical parameters. Furthermore, Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by similarity to ideal solution are applied within a multiobjective optimization framework to form a complete design methodology. In a case study, the optimized GHC design demonstrates a 10.2% reduction in <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(m_c\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>m</mi> <mi>c</mi> </msub> </math></EquationSource> </InlineEquation> with the STHC, highlighting its significant potential for sustainable soft soil improvement.</p>

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Deformation analysis and optimized design of a cement-saving grid hard crust for soft ground improvement

  • Yanxiang Guo,
  • Geng Chen,
  • Yonghui Chen,
  • Zi Ye,
  • Jiangwei Shi,
  • Gangqiang Kong

摘要

The grid hard crust (GHC), an innovative artificial structure formed by in situ solidification, comprises an upper hard crust layer and lower cement soil walls. Compared with the conventional single-thickness hard crust (STHC), the GHC achieves a lower total cement mass \(m_c\) m c while maintaining comparable settlement control. However, its application has been limited by the lack of a comprehensive design theory. This study develops an analytical model for predicting GHC deformation by integrating Timoshenko foundation beam theory, the structural displacement method, and nonlimit-state earth pressure theory. The model computes four deformation parameters: the maximum vertical displacement \(y_{\text {max}}\) y max , the differential settlement \(\Delta _{\text {max}}\) Δ max , the horizontal displacement \(x_{\text {max}}\) x max , and the stress efficiency \(\eta _{\text {load}}\) η load . The proposed model is validated against PLAXIS 2D simulations and field tests, showing good agreement. A parametric study further identifies key influencing factors and establishes preliminary design ranges for critical parameters. Furthermore, Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by similarity to ideal solution are applied within a multiobjective optimization framework to form a complete design methodology. In a case study, the optimized GHC design demonstrates a 10.2% reduction in \(m_c\) m c with the STHC, highlighting its significant potential for sustainable soft soil improvement.