Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.

This paper presents a preliminary investigation of the effectiveness of two novel magnesia-based cements in the stabilization/solidification of contaminated soils. The first is a reactive magnesia cement, which includes significant quantities of fly ash and in some cases also contains Portland cement. Magnesia cements are a recent invention and are claimed to have significant sustainability advantages over Portland cement. The second is a magnesia phosphate cement which consists of an acid phosphate source, such as potassium dihydrogen orthophosphate or triple super phosphate, and dead burned magnesia. It has mainly been used as refractory material, rapid-setting cement in a wide range structural repair works and has had limited application in stabilization/solidification treatments. Due to its low pH, it could have possible applications in facilitating biodegradation of organic contaminants in parallel to the stabilization/solidification of heavy metals in the form of very low solubility phosphates. The results presented here look specifically at the immobilization of zinc and lead in a model sand spiked with zinc chloride and lead nitrate respectively.

Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems.