Abstract
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.
Key Words
conveying concrete pipes; geomechanics; nanoparticle reinforcement; structural behavior; temperature
effects
Address
Jianfeng Li and Li Zhang: College of Civil and Transportation Engineering,Shenzhen University, Shenzhen,518060, China
A. Zamani Nouri: 2Department of Civil Engineering, ShQ. C., Islamic Azad University, Shahr-e-Qods, Iran
Li Zhang: China Railway Hefei Institute of Architectural & Municipal Engineering Design Co., Ltd, Hefei, 230041, China
Abstract
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 𝑅. The findings highlight GTBR as the most proficient predictive model with 𝑅 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.
Key Words
ensemble machine learning methods; machine learning in geotechnical engineering; online user-friendly
interface; soil analysis; soil shear strength
Address
Mohamed Rabie and Ibrahim G. Shaaban: School of Computing and Engineering, University of West London, St Mary's Road, Ealing W5 5RF, London, UK
Khaled H. Rabie: Department of Civil and Environmental Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
Abstract
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
Key Words
load transfer method; random field; reliability; pile; spatial variability
Address
Shanwei Liu, Xiaohui Tan, Jun Zhang, Fusheng Zha and Qiao Wang: School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
Abstract
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.
Key Words
finite element simulations; probabilistic seismic demand model; seismic fragility of slopes; seismic
performance; vertical component of ground motion record
Address
Dung T.P. Tran and Byungmin Kim: Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology,
50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
Youngkyu Cho: Department of Construction and Disaster Prevention Engineering, Kyungpook National University,
2559 Gyeongsang-daero, Sangju-si, Gyeongsangbuk-do, 37224, Republic of Korea
Abstract
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.
Address
Subin Kim and Yong-Joo Lee: Department of Civil Engineering, Seoul National University of Science and Technology,
232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
Dong-Wook Oh: Research and Development Institute, Hanul D&B Co., Ltd.,
178, Digital-ro, Geumcheon-gu, Seoul 08513, Republic of Korea
Suk-Min Kong: Department of Geotechnical Engineering, Korea Institute of Civil Engineering and Building Technology,
283, Goyang-daero, Ilsanseo-gu, Goyang 10223, Republic of Korea
Abstract
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.
Key Words
blast-induced ground vibration; closed-form equation; construction rock excavation; grasshopper
optimization algorithm; rock mass property; slime mould algorithm; urban areas
Address
Nafiu O. Ogunsola: Department of Mineral Resources & Energy Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju,
Jeonbuk 54896, Republic of Korea;
Department of Mining Engineering & Mine Surveying, University of Johannesburg, Johannesburg, South Africa
Kim Young-geun: Department of Mineral Resources & Energy Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju,
Jeonbuk 54896, Republic of Korea;
Terra Engineering Limited, Daejeon 34018, Republic of Korea
Kim Young-geun: Department of Mineral Resources & Energy Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju,
Jeonbuk 54896, Republic of Korea;
Department of Energy Storage & Conversion Engineering of Graduate School, Jeonbuk National University, 567 Baekje-daero,
Deokjin-gu, Jeonju, Jeonbuk 54896, Republic of Korea
