To harness available generic geotechnical databases (i.e., the so-called big indirect data) as a supplement to sparse site-specific geotechnical data from a given site, it is crucial to first address the “site recognition challenge” (i.e., identification of sites similar to a target site from the generic database). Existing methods often quantify site similarity based solely on the multivariate distribution (or cross-correlations) of geotechnical properties, without accounting for similarity in spatial variation of geotechnical properties among different sites, potentially resulting in incomplete identification outcomes. To overcome this limitation, this study proposes a novel site recognition method for automatically identifying sites similar to a target site from a generic geotechnical database, based on similarity in spatial variation of geotechnical properties among different sites in a data-driven manner. In the proposed method, spatial variation basis modes of geotechnical properties for different sites are first extracted from existing geotechnical databases using machine learning methods. Then, geotechnical data from the target site is used to identify the site with similar spatial variation patterns from the databases using sparse representation and sparsity-promotion techniques. The effectiveness of the proposed method is demonstrated using a real geotechnical database (i.e., the ISSMGE TC304 database).

Several global or regional databases for various types of soils have been developed due to their importance in engineering design and analysis. However, a database is not yet available for collapsible loess in which severe geohazards often occur. In this study, a comprehensive loess database with twelve soil parameters is compiled by collecting results of field and laboratory tests on collapsible loess from the city of Xi’an, China. Basic statistics, marginal probability distribution functions (PDFs), and a correlation matrix for loess parameters are estimated from the database. To the best of the authors’ knowledge, this is the first collapsible loess database at a municipal level. In addition, existing databases often lack sufficiently complete multivariate measurement data for a proper estimation of statistical correlations among multiple soil properties. In this study, this incomplete multivariate measurement data problem is tackled by Bayesian methods (i.e., Bayesian Gaussian mixture model and Bayesian compressive sampling (BCS) with Karhunen–Loève (KL) expansion, BCS-KL), which are illustrated and validated using the incomplete and complete subsets of the loess database, respectively. Both the Bayesian Gaussian mixture model and BCS-KL are non-parametric, and they offer a flexible way of modeling marginal PDFs and a correlation matrix from incomplete measurements in a realistic manner.

Although it is widely acknowledged that a three-dimensional (3D) model of geotechnical property variation is the cornerstone of underground digital twin, its adoption in practice remains limited, probably due to the scarcity of site-specific geotechnical data collected from site investigation. During a site investigation program, geophysical surveys are carried out sometimes, allowing for a rapid, nondestructive recognition of subsurface conditions over large areas. The abundant geophysical data obtained provides valuable complementary information for developing a reliable 3D geotechnical property model. However, no practical and rational method is available currently for a quantitative fusion of geotechnical and geophysical data in a 3D space. To tackle this challenge, this study develops an innovative 3D fusion method for efficiently integrating limited geotechnical data with high-resolution geophysical data in a 3D space. The auto- and cross-correlation of 3D geotechnical and geophysical data are exploited using joint sparse representation in the proposed method, enabling efficient exchange of information between geotechnical and geophysical data in a data-driven manner. The proposed method is demonstrated and validated using both synthetic and real-world datasets. A significant improvement is observed in the construction of 3D model of geotechnical properties after an integration of geotechnical and geophysical data.

Localized and anisotropic features extensively exist in various physical phenomena. The present work focuses on the r-adaptive parameterization technique for isogeometric analysis (IGA), which aims to acquire higher numerical accuracy while keeping the degrees of freedom constant. The principal feature is utilizing the so-called absolute principal curvature of the IGA solution surfaces to characterize numerical errors instead of posteriori error estimations, which establishes the relation between analysis results and geometric quantity. The bijectivity is a fundamental requirement for analysis-suitable parameterization. With the cooperation of a minor regularization and common line search criteria, the proposed method guarantees the bijectivity of the resulting parameterizations. The bi-level approach with two refinement levels of the same geometry is employed: a coarse level (design model) to update the parameterization and a fine level (analysis model) to perform the isogeometric simulation. Moreover, we develop several detailed algorithms for explaining the sensitivity propagation from the design model to the analysis model and analytically computing the sensitivity, which allows accurate calculation of sensitivity and enhances the robustness during a gradient-based optimization. Several examples and comparisons are presented to demonstrate the effectiveness and efficiency of the proposed method. As an application, we apply the proposed method to a two-dimensional linear heat transfer problem with a moving Gaussian heat source, which is a simplified model for the additive manufacturing application. The proposed r-adaptive technique effectively captures the thermal history of the problem.

One-part alkali-activated slag (AAS) as a binder material has a promising application in the construction industry. The properties of one-part AAS incorporating agricultural wastes have been seldom studied. In this paper, the fresh and hardened properties of one-part AAS with the addition of rice straw powder (RSP) were investigated. The reaction rate in the acceleration period of AAS is reduced by RSP. The compressive strength of the mixture decreases with the introduction of RSP, while the flexural strength increases. The porosity of the hardened mixtures becomes lower when RSP was incorporated. N-(C)-A-S-H gel was detected in the system when 4.2% RSP was present. The RSP reduces the early-age autogenous shrinkage of AAS by providing internal curing to the matrix, but its effect on long-term drying shrinkage is limited.

Aromatic micropollutants found in wastewater pose threats to human health, and it is necessary to remove them from wastewater. Cyclodextrin hydrogel is one of the most promising environmentally friendly materials for removing aromatic micropollutants from wastewater by means of adsorption. In this study, we have synthesized a cyclodextrin hydrogel designed to simultaneously adsorb a cationic dye (methylene blue, MB) and an endocrine-disrupting compound, EDC (bisphenol A, BPA) and provide insights into the design of adsorbents for multicomponent wastewater purification. Polymeric dextran-poly(acrylic acid)-β-cyclodextrin (Dex-PAA-CD) was prepared by the copolymerization of glycidyl methacrylate (GMA), methacrylate-substituted dextran (Dex-MA), acrylic acid, and vinyl-derivatized β-cyclodextrin (β-CD-MA). The adsorption of MB and BPA by the Dex-PAA-CD hydrogel presented short equilibrium time. The adsorption of MB and BPA was independent of each other, and their adsorption mechanisms were proved to be electronic interaction and cyclodextrin host-guest interaction, respectively. The maximum adsorption capacities of MB and BPA onto Dex-PAA-CD are determined to be 5.60 and 0.144 mmol/g by the Sips isotherm. The Dex-PAA-CD hydrogel retains a high adsorption capacity even after five adsorption/desorption cycles and demonstrates the feasibility of designing reusable adsorbents to remove multiple pollutants from wastewater.

A biocompatible Dex-MA/PAA hydrogel was prepared through copolymerization of glycidyl methacrylate substituted dextran (Dex-MA) with acrylic acid (AA), which was applied as the adsorbent to remove cationic dyes from aqueous solutions. Dex-MA/PAA hydrogel presented a fast adsorption rate and the removal efficiency of Methylene Blue (MB) and Crystal Violet (CV) reached 93.9% and 86.4%, respectively within one minute at an initial concentration of 50 mg L^-1. The adsorption equilibrium data fitted the Sips isotherm model well with high adsorption capacities of 1994 mg g^-1 for MB and 2390 mg g^-1 for CV. Besides, dye adsorption occurred efficiently over the pH range 3-10 and the temperature range 20-60 °C. Moreover, the removal efficiencies for MB and CV were still >95% even after five adsorption/desorption cycles which indicates the robust nature of the Dex-MA/PAA hydrogel and its potential as an eco-friendly adsorbent for water treatment.