The present research discusses coupled phenomena of temperature fluctuations and nanoparticle addition towards the mechanical properties and operation efficiencies in the pipelines used in membrane water treatment systems. More specifically, it quantifies thermal variability that can cause changes in material properties of pipes, which may increase susceptibility to deformation, stress, and fatigue, perhaps undermining system integrity over time. The study has focused on how material properties subjected to temperature changes can result in degradation and probable structural failure, which impacts performance and reliability in general. This paper discusses the addition of nanoparticles (iron oxide) into the fluid with the aim of improving heat transfer, frictional losses, and improving the durability of the materials of the pipe. With the nanoparticles expected to alleviate critical area wear and thermal stresses, the nanoparticles will enhance the lifetime and reliability of the pipeline under extreme operational conditions. The foreseen outcome will be highly improved stability and efficiency of operation within water treatment systems, especially under adverse temperature and pressure conditions. Results indicate that adding nanoparticles in the percentage range of 3-3.5% increased the dimensionless natural frequency by about 12% and the critical fluid velocity by almost 28%, providing very strong stabilizing effects. Moreover, with a Pasternak elastic foundation, system rigidity is increased by as much as 25%, thereby greatly postponing the onset of dynamic instability.

Soil shear strength, a pivotal metric in civil engineering, signifies the soil's capacity to endure shear stress before failing. The accurate determination of this measure is crucial for assessing the stability of structures situated on or embedded within the soil. Traditional methods, while integral, often prove complex and resource intensive. This has paved the way for advanced machine learning (ML) techniques to offer innovative solutions. This study delves into the efficacy of multiple ML algorithms, including Support Vector Regression (SVR), Decision Tree (DT), Random Forest Regression (RFR), Extra Tree Regressor (ETR), Gradient Tree Boosting (GTBR), and Extreme Gradient Boosting (XGBoost) in predicting undrained shear strength of soil. A comprehensive dataset, sourced from prior research, was utilized, with a strategic split of 80% for training and 20% for testing with 5 folds cross validation. Model performance was gauged using statistical metrics such as MAPE, MAE, RMSE, and R². The findings highlight GTBR as the most proficient predictive model with R² of 85.52%. Feature importance analysis revealed that variables such as the liquidity index, sample depth, and moisture content percentage played pivotal roles in shaping the model's predictions. This model has been seamlessly integrated into an online user-friendly interface, facilitating ease of access for professionals. The interface ensures a streamlined, precise tool for estimating soil shear strength.

Reliability analysis of pile groups is a hot issue in geotechnical engineering. Pile groups with dissimilar pile lengths can be adopted to alleviate the differential settlement among foundation piles and to ensure the consistency of reliability index of each foundation pile. This paper presents an approach for analyzing the reliability of pile groups with dissimilar pile lengths subjected to vertical loads in spatially variable expansive soils under rainfall. The variation of soil's matric suction with depth under rainfall infiltration is clarified using analytical formula. The Karhunen-Loève (KL) expansion method is used to discretize random fields. The pile-pile and pile-soil interactions in a pile group with dissimilar pile lengths, and the influence of the reduction of matric suction on the ultimate bearing capacity of the piles are analyzed using the modified load transfer method (LTM). The first-order reliability method (FORM) is employed to solve the reliability indices of each pile in a pile group, and the sequential component method (SCM) is adopted to estimate the reliability index of the pile group. A framework is proposed for the reliability analysis of pile groups in spatially variable unsaturated expansive soil under rainfall. Furthermore, a parameter study is carried out to assess the influence of rainfall intensity and duration on the reliability indices of pile groups.

Extensive research has been conducted on the influence of ground motion's vertical component on slope performance. However, whether this effect was substantial remains controversial. This study investigates the effect of the vertical component of ground motion records on the seismic fragility of slopes using finite element simulations. We consider three slope models with frictional soils subjected to two sets of input motions comprising a subset of ground motion records in the Next Generation Attenuation West 2 database (Sets H and HV consist of 300 horizontal components only and 300 combined horizontal and vertical components of the records, respectively). The maximum permanent seismic slope displacements computed from Set HV are mostly greater than those from Set H, particularly at a small horizontal-component peak ground acceleration (PGAH). The seismic fragility curves of the slopes from Set HV, calculated by a probabilistic seismic demand model, are generally higher than those using Set H for three threshold values (5, 15, and 30 cm), with a pronounced distinction observed from intermediate to high levels of PGAH. This outcome indicates that including the vertical component in dynamic finite element simulations affects the seismic fragility of slopes, expected to contribute to improving seismic hazard and resilience assessments.

As urban tunnel construction becomes increasingly complex, accurately predicting ground shear deformation is crucial for ensuring structural stability. This study develops a machine learning-based model to predict shear deformation in soil during twin tunnel excavation. Numerical analysis using PLAXIS 2D, laboratory model tests, and machine learning models (XGBoost and LightGBM) were combined to assess deformation patterns. The results of inverse analysis closely matched the experimental data, confirming the validity of the numerical approach. The predictive models demonstrated high accuracy, with XGBoost outperforming LightGBM, achieving an error rate of 9.3% compared to 46.1% for LightGBM. Feature importance analysis revealed that pile length, tunnel spacing, and vertical offset significantly influenced deformation behavior. Additionally, hyperparameter tuning using Optuna enhanced the models' predictive performance. However, while the models effectively captured overall deformation trends, they exhibited limitations in accurately predicting localized deformation near the tunnel sidewalls and pile tip. This study highlights the potential of machine learning for geotechnical applications, particularly in underground construction. Future research should expand the dataset with diverse ground conditions and apply explainable AI techniques to enhance model interpretability. The findings contribute to improving the reliability and efficiency of tunnel design and construction in urban environments.

Prediction and minimization of the adverse consequences of blasting, particularly blast-induced ground vibration (BIGV), are important tasks that largely determine the success of mining, tunneling, and civil engineering projects. However, most previous studies on the prediction of the BIGV have focused on mining and tunneling, whereas the BIGV of construction excavation blasting, including urban and rural highway construction and urban residential land development and redevelopment projects, have rarely been predicted. BIGV from construction project sites are critical because of their proximity to urban and rural dwellings, and important public utilities. This study introduces a novel hybrid machine learning (ML) model of an artificial neural network (ANN) optimized using the slime mould algorithm (SMA) and grasshopper optimization algorithm (GOA) to forecast and minimize BIGV generation from multiple highways and urban residential land development and redevelopment project sites in South Korea. In this study, 115 blasting events from construction sites in South Korea were monitored and their parameters, peak particle velocity, and rock mass rating were recorded. A comparison was made between the newly introduced ANN-SMA and ANN-GOA models, other developed machine learning (ML) models, and three empirical models. The newly introduced models significantly outperformed other models. The suggested hybridized models were transformed into adjustable and user-friendly explicit equations that can assist field and blasting engineers working at construction sites in accurately predicting BIGV during construction. The models were validated for practical engineering applications using 20 separate datasets that were not employed for model development. Finally, the relevance importance appraisal of the model inputs was performed using the cosine amplitude method (CAM), and the rock mass rating (RMR) had a significant influence on the forecasted BIGV. The findings of this study could aid engineers and researchers in accurately estimating the potential PPV values in construction excavation projects before actual blasting, which will help in advance planning to enhance blast design and mitigate conflicts and disagreements between mines and local communities.