Potential of Computer-Aided Engineering in the Design of Ground-Improvement Technologies

The progress status of jet-grouting construction during the construction phase is difficult to verify and even after the completion of construction, it can be verified only by empirical methods. This study attempted to recreate a realistic simulation result of the middle-pressure jet-grouting method by establishing a computer-aided engineering (CAE) system from the planning/design stage of the ground model and verifying the validity of the construction process after the model was analyzed by the moving particle semi-implicit (MPS) method. The governing parameters for the ground were determined by the MPS simulation of the unconfined compression test. The construction simulation was analyzed and the results were validated by visual confirmation of the related phenomena, such as the soil-improved body formation and mud discharge. To verify the accuracy of the mud discharge phenomenon, three different probe regions were set above the model ground and the amount of mud discharge generated in each region was computed before drawing an overall conclusion of the study. A soil-improvement body of approximately 0.38 m3 was observed to have formed at the end of the study and the highest mud discharge particle number measured, for instance, was 896. This study is expected to serve as a guideline for further studies on simulation-based research

Impact of the Boreholes on the Surrounding Ground

The infrastructures that were constructed decades ago do not meet the present structural benchmark, and they need to be demolished. In order to reclaim these lands, the existing pile foundations must be removed; otherwise, the land will lose its value. Since the piles are pulled out, vacant spaces are created in the ground. This causes the surrounding ground to experience settlement, jeopardizing its stability. The degree of influence depends upon the number of boreholes, the saturated condition of the ground, the time period of the vacant condition, the presence of loading, etc. It is important to understand the scope of the probable settlement under various situations. This study focused on determining the amount of displacement and its range for three different saturated soil types under loaded and unloaded conditions using the finite element method (FEM) analysis. It was observed that stiff ground underwent maximum deformation, while soft ground experienced the maximum influence from external factors. Moreover, the presence of loading not only increased the displacement amount and range, but it also caused a change in the location of the maximum displacement

Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting

In the field of geotechnical engineering, the problems of liquefaction and land subsidence are of major concern. In order to mitigate or prevent damage from liquefaction, the chemical injection method is actively used as one of the countermeasures for ground improvement. However, a complete understanding of the long-term sustainability of improved grounds is still unavailable due to a lack of knowledge of the influencing parameters. Thus, the chances of chemical injection accidents cannot be ruled out. In this study, the compressive strength of improved grounds by the granulated blast furnace slag (GBFS), one of the grouting materials used in the chemical injection method, was evaluated and used for a time-series prediction of long-term sustainability. The objective of this study was to evaluate the accuracy and validity of the prediction method by comparing the prediction results with the test results. The study was conducted for three different models, namely, the autoregressive integrated moving average (ARIMA) model, the state-space representation (SSR) model, and the machine learning predictive (MLP) model. The MLP model produced the most reliable results for the prediction of long-term data when the input information was sufficient. However, when the input data were scarce, the SSR model produced more reliable results overall. Meanwhile, the ARIMA model generated the highest degree of errors, although it produced the best results compared to the other models depending on the criteria. It is advised that studies should be continued in order to identify the parameters that can affect the long-term sustainability of improved grounds and to simulate various other models to determine the best model to be used in all situations. However, this study can be used as a reference for the selection of the best prediction model for similar patterned input data, in which remarkable changes are observed only at the beginning and become negligible at the end

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous