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<p>Soil cracking poses significant challenges to the integrity of slopes and earthen infrastructure. This study presents a dataset for comprehensive analysis of desiccation cracks in soils (D-CRACKS), aiming to enhance the understanding of this complex phenomenon. The dataset compiles 1,000 images from laboratory tests of soil desiccation cracking collected from 41 studies in the literature. D-CRACKS is built using a Structured Query Language (SQL)-based database management system, enabling efficient data retrieval, filtering, and querying. Each image is linked to detailed metadata, including testing conditions, soil properties, boundary constraints of the tested samples, environmental conditions, and admixture properties. Following data cleaning, images were analyzed to extract key crack properties such as crack area, crack ratio, crack length, and average crack width. Statistical analyses were performed to examine variability and trends across soil types and testing conditions. D-CRACKS offers a structured and scalable foundation for investigating soil cracking and offers substantial value for future research, particularly for developing data-driven predictive models and validating physics-based numerical simulations of soil cracking.</p>

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Dataset for Comprehensive Analysis of Desiccation Cracks in Soils

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Amirali Asadian,
  • Farshid Vahedifard,
  • Chao-Sheng Tang

摘要

Soil cracking poses significant challenges to the integrity of slopes and earthen infrastructure. This study presents a dataset for comprehensive analysis of desiccation cracks in soils (D-CRACKS), aiming to enhance the understanding of this complex phenomenon. The dataset compiles 1,000 images from laboratory tests of soil desiccation cracking collected from 41 studies in the literature. D-CRACKS is built using a Structured Query Language (SQL)-based database management system, enabling efficient data retrieval, filtering, and querying. Each image is linked to detailed metadata, including testing conditions, soil properties, boundary constraints of the tested samples, environmental conditions, and admixture properties. Following data cleaning, images were analyzed to extract key crack properties such as crack area, crack ratio, crack length, and average crack width. Statistical analyses were performed to examine variability and trends across soil types and testing conditions. D-CRACKS offers a structured and scalable foundation for investigating soil cracking and offers substantial value for future research, particularly for developing data-driven predictive models and validating physics-based numerical simulations of soil cracking.