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Articles 1 - 30 of 188
Full-Text Articles in Civil Engineering
Cross-Dataset Stability Assessment Of A Fixed-Configuration Catboost Model Across Heterogeneous Civil Engineering Material Datasets, Arslan Qayyum Khan, Ali Raza, Amorn Pimanmas
Cross-Dataset Stability Assessment Of A Fixed-Configuration Catboost Model Across Heterogeneous Civil Engineering Material Datasets, Arslan Qayyum Khan, Ali Raza, Amorn Pimanmas
Civil Engineering Faculty Publications
Machine learning (ML) has increasingly been applied in civil and structural engineering for predicting material and structural performance. However, most existing studies either benchmark several algorithms within a single dataset or review such findings, leaving the cross-dataset stability of one fixed model configuration largely unquantified. This study proposes a generalized ML evaluation framework to investigate the robustness and stability of a fixed-configuration model across multiple civil engineering material datasets. Ten datasets representing diverse structural and material problems were compiled, including 3D printed concrete (3DPC), conventional and advanced concrete including normal and high strength concrete (NHSC), recycled aggregate concrete (RAC), ultra-high-performance …
Evaluating The Impacts Of Extreme Temperature Events On The Us Rail Network, Gabby Gutierrez, Gasser G. Ali, Constantine Tarawneh
Evaluating The Impacts Of Extreme Temperature Events On The Us Rail Network, Gabby Gutierrez, Gasser G. Ali, Constantine Tarawneh
Civil Engineering Faculty Publications
The United States railway is a critical part of the national infrastructure and economy. Heatwaves are a major weather event that can cause major disruptions and affect the stable operation of the railway. Failure in part of the rail network may have a ripple effect on the rest of the network. As such, there is a need to study the effect of weather on rail networks that require re-routing. The goal of this paper is to investigate the vulnerability of the contiguous United States rail infrastructure against disruptions to operations caused by extreme heat events. This goal is achieved by …
Estimating Highway Rehabilitation Projects Using Machine Learning, Samantha Ocaña, Silvia Flores-Osuna, Gasser G. Ali, Constantine Tarawneh
Estimating Highway Rehabilitation Projects Using Machine Learning, Samantha Ocaña, Silvia Flores-Osuna, Gasser G. Ali, Constantine Tarawneh
Civil Engineering Faculty Publications
Highways are a crucial part of the national infrastructure. As such, they are costly to construct and maintain; challenges such as deterioration, limited budgets, and cost uncertainty create a need for improved cost prediction of rehabilitation projects. This is especially true in the preliminary stage when all factors are unknown. Accordingly, this paper proposes a model to predict the cost of highway rehabilitation projects. A machine learning model is trained on large and publicly available datasets released by the Texas Department of Transportation (TxDOT), the National Oceanic and Atmospheric Administration (NOAA), and the Federal Reserve of Economic Data (FRED). These …
Residential Cooling Energy Efficiency During Heatwaves In South Texas, Rosa A. Castan, Gasser G. Ali
Residential Cooling Energy Efficiency During Heatwaves In South Texas, Rosa A. Castan, Gasser G. Ali
Civil Engineering Faculty Publications
Heatwaves have become increasingly intense and frequent in recent years. As a result, residents are forced to rely on air-conditioning systems, a major contributor to high energy consumption. South Texas is one of the regions that has been affected by historically extreme heat. This paper explores the challenges this region has faced due to heatwaves and the strategies adopted to mitigate their effects. It investigates the use of cooling technologies, energy-related habits, and investment in home construction to reduce indoor heat and manage electricity bills. A literature review is performed to help identify relevant factors related to maximizing energy efficiency. …
Physics-Informed Neural Networks For Early-Warning Fatigue Damage Detection And Localization In A Steel Railway Bridge Using Sparse Ambient Monitoring Data, Arslan Qayyum Khan, Ali Raza, Amorn Pimanmas
Physics-Informed Neural Networks For Early-Warning Fatigue Damage Detection And Localization In A Steel Railway Bridge Using Sparse Ambient Monitoring Data, Arslan Qayyum Khan, Ali Raza, Amorn Pimanmas
Civil Engineering Faculty Publications
Aging steel railway bridges accumulate fatigue damage at locations that periodic inspection rarely reaches and that conventional vibration monitoring detects only after severe stiffness loss. Existing physics-informed learning methods for structural health monitoring are mostly validated on synthetic data and single snapshots, leaving their performance on continuous field data with verified damage untested. This study develops a strain-based monitoring approach, supported by a physics-informed neural network (PINN), for early detection of fatigue damage in a steel railway bridge. The measured strain is compensated for temperature using a model calibrated on healthy-state data, the resulting anomaly is tracked through a statistical …
Human-Centered Electric Vehicle Adoption Framework For Smart Mobility: Modeling Perceived Range And Charging Anxiety As A Psychological Barrier, Fatemeh Nazari, Abolfazl (Kouros) Mohammadian, Thomas Stephens
Human-Centered Electric Vehicle Adoption Framework For Smart Mobility: Modeling Perceived Range And Charging Anxiety As A Psychological Barrier, Fatemeh Nazari, Abolfazl (Kouros) Mohammadian, Thomas Stephens
Civil Engineering Faculty Publications
Electric vehicles (EVs) offer a transformative pathway toward reducing the environmental, economic, and health-related externalities of internal combustion engine vehicles in urban settings. Despite substantial advances in battery technology, charging infrastructure expansion, and supportive policy incentives, EV penetration remains limited which poses challenges for smart and sustainable mobility planning. A critical yet insufficiently modeled barrier to adoption lies in the psychological perceptions surrounding electric driving range and charging reliability, which is commonly framed as “range anxiety,” but more broadly reflecting perceived range and charging anxiety. To address this gap, this study introduces a latent psychological construct capturing individuals’ perceived range …
A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari
A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari
Civil Engineering Faculty Publications
Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. …
Ai-Driven Detection Of Failure Modes In Thermoplastic Composites Using Acoustic Emission Techniques, M. M. Shahzamanian, Li Ai, Paul Ziehl
Ai-Driven Detection Of Failure Modes In Thermoplastic Composites Using Acoustic Emission Techniques, M. M. Shahzamanian, Li Ai, Paul Ziehl
Civil Engineering Faculty Publications
Composite materials in aircraft structures can suffer impact damage that leaves barely visible yet structurally significant defects, which degrade mechanical performance, especially under compressive loads. Existing methods for identifying failure modes in compression after impact (CAI) tests using acoustic emission (AE) data are limited in accuracy and scope. This study introduces an approach combining AE sensing with a heterogeneous ensemble convolutional neural network (CNN) to detect and classify failure mechanisms in impacted composite specimens. The novelty of this work lies in employing Red, Green, and Blue (RGB) wavelet images, produced through continuous wavelet transform (CWT) of AE signals, as inputs …
Hybrid Deep Learning Model For Accurate Settlement Forecasting Of Metro Tracks Under Canal Diversion Engineering, Haijie He, Zhenlin Wang, Sifan Shen, Shiyu Sheng, Jing Zhang, Chuang He, Huafeng Shan, Qiongfang Zhang, Li Ai
Hybrid Deep Learning Model For Accurate Settlement Forecasting Of Metro Tracks Under Canal Diversion Engineering, Haijie He, Zhenlin Wang, Sifan Shen, Shiyu Sheng, Jing Zhang, Chuang He, Huafeng Shan, Qiongfang Zhang, Li Ai
Civil Engineering Faculty Publications
The structural stability of metro systems is essential for safe and reliable urban rail operation. Large-scale underground construction may influence existing metro lines, making accurate settlement prediction necessary. Traditional empirical and numerical methods often fail to capture long-term settlement behavior. This study predicts track bed settlement of Hangzhou Metro Line 1 using monitoring data collected during the Grand Canal diversion construction. A hybrid model (CEEMDAN-BWO-BiLSTM-ATT model) integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Beluga Whale Optimization, Bidirectional Long Short-Term Memory, and an attention mechanism is developed. Results from four monitoring points along the up line show good performance, …
Intelligent Cost-Optimized Mix Design Prediction And Engineered Strength System For Geopolymer Concrete: A Machine Learning-Based Recommender System, Yuvaraj Natarajan, K.R. Sri Preethaa, V. Danushkumar, Syed Muhammad Oan Naqvi, M. Shyamala Devi, Karen Lozano, Bubryur Kim, Jinwoo An
Intelligent Cost-Optimized Mix Design Prediction And Engineered Strength System For Geopolymer Concrete: A Machine Learning-Based Recommender System, Yuvaraj Natarajan, K.R. Sri Preethaa, V. Danushkumar, Syed Muhammad Oan Naqvi, M. Shyamala Devi, Karen Lozano, Bubryur Kim, Jinwoo An
Civil Engineering Faculty Publications
Geopolymer concrete is a promising low-carbon alternative to ordinary Portland cement concrete, but its practical use is limited by complex mix-design requirements and limited cost-aware decision-support tools. This study developed the Intelligent Cost-Optimized Mix Design Prediction and Engineered Strength System (iCOMPRESS), a machine learning-based recommender system that integrates 28-day compressive strength prediction, cost optimization, and compositionally diverse mixture recommendation. A database of 443 literature-derived mixtures was used to train a hyperparameter-optimized Random Forest model with domain-informed features related to binder chemistry, alkaline activation, water content, aggregates, and curing conditions. The model achieved a five-fold cross-validation mean absolute error (MAE) of …
Deep Learning-Based Structural Displacement Identification And Quantification Under Target Feature Loss, Lishuai Zhu, Guangcai Zhang, Qun Xie, Zhen Peng, Li Ai, Ruijun Liang, Taochun Taochun Yang
Deep Learning-Based Structural Displacement Identification And Quantification Under Target Feature Loss, Lishuai Zhu, Guangcai Zhang, Qun Xie, Zhen Peng, Li Ai, Ruijun Liang, Taochun Taochun Yang
Civil Engineering Faculty Publications
Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features, making it difficult for traditional methods to ensure high accuracy and robustness. Therefore, this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm. By combining deep learning techniques with traditional template matching methods, the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination. Specifically, in the edge-orientation gradient matching stage, the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are …
A Novel Optuna-Vmd-Ml Framework For Enhanced Settlement Prediction Of Buildings Around Foundation Pits, Jing Zhang, Zhenlin Wang, Shiyu Sheng, Sifan Shen, Chuang He, Huafeng Shan, Haijie He, Li Ai
A Novel Optuna-Vmd-Ml Framework For Enhanced Settlement Prediction Of Buildings Around Foundation Pits, Jing Zhang, Zhenlin Wang, Shiyu Sheng, Sifan Shen, Chuang He, Huafeng Shan, Haijie He, Li Ai
Civil Engineering Faculty Publications
The prediction of building settlement around foundation pit is of vital importance to ensure the safety and stability of urban construction projects. However, current predictions of buildings surrounding foundation pit face numerous challenges. Therefore, this study proposes a framework that integrates Optuna-based hyperparameter optimization, variational mode decomposition (VMD), and machine learning (ML) for accurate and timely settlement prediction in practical engineering scenarios, referred to as the Optuna–VMD–ML framework. Optuna is employed to tune the hyperparameters of both VMD and the ML models; the Optuna-tuned VMD decomposes the settlement time series into mode components, which are then used to train the …
Autonomous Vehicle Adoption Behavior And Safety Concern: A Study Of Public Perception, Fatemeh Nazari, Mohamadhossein Noruzoliaee, Abolfazl (Kouros) Mohammadian
Autonomous Vehicle Adoption Behavior And Safety Concern: A Study Of Public Perception, Fatemeh Nazari, Mohamadhossein Noruzoliaee, Abolfazl (Kouros) Mohammadian
Civil Engineering Faculty Publications
Realizing the economic and societal benefits of autonomous vehicles (AVs) hinges on widespread public acceptance. However, existing research offers limited insights into two key behavioral factors shaping AV acceptance, namely, perceived AV safety concern and travel behavior, the latter reflecting how heterogenous mobility patterns influence the AV acceptance. These factors are often treated as exogenous, limiting insight into their true behavioral interdependencies with AV acceptance and their distinct behavioral roots. This study addresses these gaps by introducing a recursive trivariate econometric model that jointly estimates AV acceptance, perceived safety concern, and current travel behavior (proxied by annual vehicle-miles traveled or …
Evaluating The Impact Of Aggregate Size And Reinforcement On Alkali-Silica Reaction In Concrete Through Nondestructive Testing Techniques, Li Ai, David Bianco, Vafa Soltangharaei, Rafal Anay, Mahmoud Bayat, Paul Ziehl
Evaluating The Impact Of Aggregate Size And Reinforcement On Alkali-Silica Reaction In Concrete Through Nondestructive Testing Techniques, Li Ai, David Bianco, Vafa Soltangharaei, Rafal Anay, Mahmoud Bayat, Paul Ziehl
Civil Engineering Faculty Publications
This research investigates different nondestructive evaluation (NDE) methods to assess concrete under alkali-silica reaction (ASR) development. Four methods including acoustic emission (AE), ultrasonic pulse velocity (UPV), crack width measurement, and strain measurement were applied to reactive and control specimens under accelerated ASR conditioning. The innovation lies in using NDE methods to monitor concrete with varying aggregate sizes, quantifying method sensitivity through measured indices, and highlighting the effectiveness of each method to capture ASR development. The results indicate that the unconfined reactive fine-aggregate sample exhibited isotropic expansion, while coarse-aggregate specimens showed around 50 % greater longitudinal expansion and AE cumulative signal …
Explainable Machine Learning And Life Cycle Assessment For Sustainable Design Of Fiber-Reinforced Asphalt Concrete, Xiao Tan, Jianglei Xing, Soroush Mahjoubi, Pengwei Guo, Ziyao Wei, Yuan Wang, Jie Ren, Li Ai, Weina Meng, Yi Bao
Explainable Machine Learning And Life Cycle Assessment For Sustainable Design Of Fiber-Reinforced Asphalt Concrete, Xiao Tan, Jianglei Xing, Soroush Mahjoubi, Pengwei Guo, Ziyao Wei, Yuan Wang, Jie Ren, Li Ai, Weina Meng, Yi Bao
Civil Engineering Faculty Publications
Conventional asphalt concrete has a limited lifespan due to cracking, deformation, and environmental degradation, driving the development of fiber-reinforced asphalt concrete (FRAC). However, key gaps remain in current data-driven FRAC studies due to small and homogeneous datasets, “black-box” machine learning models, and trade-offs between mechanical-sustainable performance, failing to provide a transparent understanding of features governing FRAC behaviors. This paper proposes a framework integrating explainable artificial intelligence and life cycle assessment (LCA) to advance mechanical and sustainable design of FRAC. A dataset of 2490 laboratory samples covers 15 input features and 3 mechanical outputs. Eight machine learning models, along with a …
Models Of Change: Communicating To Your Funder And Stakeholders How You Believe Your Project Will Succeed, Jean Mclaughlin, Thuy Vu, Noe Vargas Hernandez, Arturo A. Fuentes
Models Of Change: Communicating To Your Funder And Stakeholders How You Believe Your Project Will Succeed, Jean Mclaughlin, Thuy Vu, Noe Vargas Hernandez, Arturo A. Fuentes
Civil Engineering Faculty Publications
No abstract provided.
The Equity Implications Of Pecuniary Externalities On An Electric Grid, Charles Sims, Gasser G. Ali, J Scott Holladay, Tim Roberson, Chien-Fei Chen, Islam H. El-Haddad
The Equity Implications Of Pecuniary Externalities On An Electric Grid, Charles Sims, Gasser G. Ali, J Scott Holladay, Tim Roberson, Chien-Fei Chen, Islam H. El-Haddad
Civil Engineering Faculty Publications
The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of rooftop solar adoption, we show how this pecuniary externality between low- and high-income customers increases low-income electricity bills by 10% in an area with some of the highest poverty rates in the United …
Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Mohamed Gaber, Mohamed Abdel-Raheem
Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Mohamed Gaber, Mohamed Abdel-Raheem
Civil Engineering Faculty Publications
As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …
Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An
Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An
Civil Engineering Faculty Publications
Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based …
Carbon-Neutral Concrete: A Review On Carbon Capture, Storage, And Utilization Technologies, Syed Muhammad O Naqvi, Muhammad Daniyal Raza, Syed Muhammad Bilal Haider
Carbon-Neutral Concrete: A Review On Carbon Capture, Storage, And Utilization Technologies, Syed Muhammad O Naqvi, Muhammad Daniyal Raza, Syed Muhammad Bilal Haider
Civil Engineering Faculty Publications
Concrete industry is responsible for approximately 7% of total CO2 emissions around the globe making it a critical target for decarbonization. This review study evaluates carbon capture, utilization, and storage (CCUS) technologies applicability in concrete industry with a focus on direct air capture (DAC), CO2 curing, mineral carbonation, and incorporation of carbonated recycled aggregates and alternative binders. Emphasis is placed on the mechanisms of various CCUS technologies, economic feasibility, environmental benefits, mechanical performance, and current challenges in their application and scalability aiming to optimize the structural efficiency, carbon uptake, and cost of concrete structures. Case studies from industrial implementations, such …
Performance Analysis Of Flapping-Foil Energy Harvesters Under Shear-Flow Conditions, Maqusud Alam, Bubryur Kim, Shehnaz Akhtar, Sujeen Song, Zengshun Chen, Jinwoo An
Performance Analysis Of Flapping-Foil Energy Harvesters Under Shear-Flow Conditions, Maqusud Alam, Bubryur Kim, Shehnaz Akhtar, Sujeen Song, Zengshun Chen, Jinwoo An
Civil Engineering Faculty Publications
Flapping-foil energy harvesters represent a promising technology for extracting energy from fluid flows, although their performance under shear-flow conditions is poorly understood. To address this research gap, we investigated the impact of power-law shear flow on the performance of flapping-foil energy harvesters. We conducted numerical simulations at different Reynolds numbers (Re = 1000, 50 000, and 500 000) under uniform-flow and shear-flow conditions. Based on the results, shear flow minimally affected the power outputs at Re = 1000 and 50 000, where viscous forces dominate the flow dynamics. However, at Re = 500 000, shear flow significantly reduced the …
Acoustic Emission Sensing For Assessing Compressive Strain In Impacted Thermoplastic Composites, M. M. Shahzamanian, Li Ai, Sydney Houck, Md Mushfiqur Rahman Fahim, Sourav Banerjee, Paul Ziehl
Acoustic Emission Sensing For Assessing Compressive Strain In Impacted Thermoplastic Composites, M. M. Shahzamanian, Li Ai, Sydney Houck, Md Mushfiqur Rahman Fahim, Sourav Banerjee, Paul Ziehl
Civil Engineering Faculty Publications
Composite materials used in aircraft during in-flight service are vulnerable to impacts, which can lead to undetected damage and deterioration. Impact incidents can cause considerable structural harm that might not be immediately apparent, resulting in a decline in the mechanical properties of material and overall performance. There are currently limited advanced models capable of assessing the state of compressive strain (or stress) in a compression after impact (CAI) test based on acoustic emission (AE) data. Developing such a model may enable real-time monitoring and evaluation during in-flight service, providing users with alerts when composite material failure is approaching. This paper …
The Application Of Vetiver Grass In Natural Disaster Mitigation And Environmental Protection In Vietnam: A Bibliometric Analysis And Literature Review, Truc Phan, Tan Nguyen, Thang Pham, Bich T. Luong, Huong Nguyen
The Application Of Vetiver Grass In Natural Disaster Mitigation And Environmental Protection In Vietnam: A Bibliometric Analysis And Literature Review, Truc Phan, Tan Nguyen, Thang Pham, Bich T. Luong, Huong Nguyen
Civil Engineering Faculty Publications
Vetiver grass (Vetiveria zizanioides or Chrysopogon zizanioides) is a versatile tropical plant widely recognized for its applications in environmental protection and natural disaster mitigation. In Vietnam, where natural disasters such as floods and landslides are frequent, particularly along highways, Vetiver grass has proven to be an effective bioengineering solution. This paper provides a comprehensive review of the applications and benefits of Vetiver grass in preventing soil erosion and stabilizing slopes along Vietnam’s transportation systems. A bibliometric analysis of 555 Scopus-indexed publications (2000 – 2024) was conducted using VOSviewer software to identify research trends, key themes, and knowledge gaps. The findings …
Planning And Policy For Safer Roads With Autonomous Vehicles: Moral Decision Making Behavior In Dilemma-Inducing Situations, Fatemeh Nazari, Mohamadhossein Noruzoliaee
Planning And Policy For Safer Roads With Autonomous Vehicles: Moral Decision Making Behavior In Dilemma-Inducing Situations, Fatemeh Nazari, Mohamadhossein Noruzoliaee
Civil Engineering Faculty Publications
This study develops a Dynamic Bayesian Network (DBN) framework to examine how public confidence in autonomous vehicle (AV) safety and willingness-to-ride respond to policy interventions in crash-imminent pedestrian–passenger prioritization scenarios. Using stated preferences survey data from San Francisco (SF) and San Antonio (SA), the model integrates baseline attitudes, socio-demographic factors, and policy conditions to simulate both intra-slice and inter-slice dependencies. Results from empirical-mix simulations indicate that SF respondents, despite having higher baseline confidence and willingness-to-ride, exhibit greater sensitivity to policies prioritizing pedestrians, with significant declines across all scenarios. By contrast, SA respondents show comparatively stable and modestly positive shifts, particularly …
Effects Of Eogo In Metakaolin-Based Geopolymer, Chaewon Lee, Hoyoung Lee, Jinwoo An, Boo Hyun Nam
Effects Of Eogo In Metakaolin-Based Geopolymer, Chaewon Lee, Hoyoung Lee, Jinwoo An, Boo Hyun Nam
Civil Engineering Faculty Publications
Geopolymer concrete uses a geopolymer binder instead of traditional Portland cement; thus, it reduces carbon emissions by a significant amount. In this study, Edge-Oxidized Graphene Oxide (EOGO), a carbon-based nanomaterial, was added into a metakaolin-based geopolymer, and its effect on the mechanical and rheological properties of the mixture was investigated. EOGO was added into the mixture at 0% (control), 0.1%, 0.5%, and 1% of the metakaolin mass. Several experiments were conducted to characterize the properties of the metakaolin–EOGO (MKGO) geopolymer, including its compressive strength, free–free resonance column (FFRC), void content, water absorption, setting time, flow, and rheology. It was found …
Research On Node-Improved Energy Dissipation Wear Model For Fretting Fatigue Prediction In Railway Press-Fit Shaft, Hang Wang, Lijun Zhang, Weijian Zhang, Hongtao Li, Hong Chi, Kai Yang, Jixu Zhou, Li Ai
Research On Node-Improved Energy Dissipation Wear Model For Fretting Fatigue Prediction In Railway Press-Fit Shaft, Hang Wang, Lijun Zhang, Weijian Zhang, Hongtao Li, Hong Chi, Kai Yang, Jixu Zhou, Li Ai
Civil Engineering Faculty Publications
As a key component of the traveling system of high-speed trains, the axle is crucial for its safe operation. The current research on the fretting wear and fatigue development of shafts suffers from the problems of low local simulation accuracy of the wear model and the lack of detection and validation methods for the dynamic expansion of wear and fatigue. To this end, this study firstly proposes a new node-improved form of energy dissipation wear model, which is more sensitive to the contact behavior of the asperity body in the overfilled region and the energy transfer process; it exhibits wear …
Numerical Analysis Of The Stress–Deformation Behavior Of Soil–Geosynthetic Composite (Sgc) Masses Under Confining Pressure Conditions, Truc Phan, Meen-Wah Gui, Thang Pham, Bich T. Luong
Numerical Analysis Of The Stress–Deformation Behavior Of Soil–Geosynthetic Composite (Sgc) Masses Under Confining Pressure Conditions, Truc Phan, Meen-Wah Gui, Thang Pham, Bich T. Luong
Civil Engineering Faculty Publications
The growing application of soil–geosynthetic composites (SGCs) in geotechnical engineering has highlighted the critical role of reinforcement spacing in enhancing structural performance. This study presents a numerical investigation of the stress–deformation behavior of SGC masses under working stress and failure load conditions, considering both confining and unconfined pressure scenarios. A finite element (FE) model was developed to analyze stress distribution, reinforcement strain profiles at varying depths, and lateral displacement at open facings. Results revealed that vertical stresses in reinforced and unreinforced soil masses were nearly identical, while lateral stresses increased notably in reinforced masses, particularly near reinforcement layers and open …
Frequency Up-Conversion Electromagnetic Energy Harvester For Generating Electrical Power From Vibration Of Beams Under Moving Load, Md Ismail Monsury, Adamaris Sanchez, Mohsen Amjadian, Constantine Tarawneh
Frequency Up-Conversion Electromagnetic Energy Harvester For Generating Electrical Power From Vibration Of Beams Under Moving Load, Md Ismail Monsury, Adamaris Sanchez, Mohsen Amjadian, Constantine Tarawneh
Civil Engineering Faculty Publications
This paper studies a single-resonator electromagnetic energy harvester that employs an impact-driven frequency up-conversion mechanism to convert low-frequency vibrations of a multi-span beam, subjected to successive moving loads, into electrical power. The harvester consists of a cantilever beam made of plastic, serving as the resonator, with a thick square copper coil attached to its free end. This coil moves relative to two stationary cubic neodymium permanent magnets, each positioned on one side of the coil. To expand the harvester’s operational bandwidth, a stopper is positioned beneath the resonator, inducing controlled mechanical impacts as the cantilever beam vibrates. These impacts effectively …
Numerical Study Of A Locally Resonant Frictional Metamaterial For Seismic Vibration Control Of Liquid Storage Tanks, Shayan Khosravi, Mohsen Amjadian
Numerical Study Of A Locally Resonant Frictional Metamaterial For Seismic Vibration Control Of Liquid Storage Tanks, Shayan Khosravi, Mohsen Amjadian
Civil Engineering Faculty Publications
Liquid storage tanks (LSTs) are critical infrastructure components, storing essential fluids in facilities such as oil refineries and nuclear power plants. However, their vulnerability to seismic damage, including tank wall buckling and anchor uplift due to fluid-structure interaction and sloshing dynamics, necessitates advanced protective measures. This study introduces the Locally Resonant Frictional Metamaterial (LRFM) system as an innovative seismic base-isolation (SBI) technology to mitigate earthquake-induced effects on LSTs. The LRFM system consists of a periodic lattice framework with friction-based resonators designed to attenuate seismic waves by generating low-frequency bandgaps (0–20 Hz), which is a critical range for mitigating impulsive seismic …
Experimental Investigation On Metakaolin/Coal Fly Ash-Based Porous Geopolymer Grouting Material For Geotechnical Applications, Karla Sierra, Philip Park, Chu-Lin Cheng, Yong Je Kim, Jae-Hoon Hwang, Bubryur Kim, Boo Hyun Nam, Jinwoo An
Experimental Investigation On Metakaolin/Coal Fly Ash-Based Porous Geopolymer Grouting Material For Geotechnical Applications, Karla Sierra, Philip Park, Chu-Lin Cheng, Yong Je Kim, Jae-Hoon Hwang, Bubryur Kim, Boo Hyun Nam, Jinwoo An
Civil Engineering Faculty Publications
This research investigates the development of a porous geopolymer cement grout for soil grouting applications, aiming to reduce carbon emissions associated with Portland cement while maintaining critical performance characteristics such as strength and permeability. Class F fly ash and metakaolin were used as aluminosilicate precursors, activated by sodium silicate and sodium hydroxide solutions. The addition of hydrogen peroxide served as a foaming agent to introduce porosity. Compressive strength and porosity were evaluated, with results showing that metakaolin significantly increased compressive strength due to its smaller particle size and higher reactivity. A higher molarity of sodium silicate enhanced strength by reducing …