Civil Engineering Commons^™

Research Status And Prospects Of Monitoring Technology For Large-Span Cable-Stayed Bridges Based On Machine Learning, Liu Guoliang, Liu Guokun, Yan Donghuang, Wang Wenxi, Wang Qishun

Journal of China & Foreign Highway

Machine learning and intelligent optimization algorithms have been increasingly applied to construction and health monitoring of long-span cable-stayed bridges. Based on the construction history of cable-stayed bridges both domestically and internationally, an overview of the origin and development process of cable-stayed bridges was provided. Firstly, from the perspective of the entire life cycle of bridges, bridge monitoring was divided into construction period monitoring and operation period monitoring. The applications of mainstream construction monitoring methods in large cable-stayed bridge projects were elaborated, and the specific composition of bridge health monitoring systems was clarified. Secondly, the basic principles of several machine learning …

Ensemble Learning Framework For Predicting Close Proximity Tire–Pavement Noise On Expressways, Woo Young Cho, Jin Hwan Kim, Guk Gon Song, Kyungnam Kim, Youngguk Seo

Faculty Articles

Traffic noise is a critical public health concern affecting millions of highway users and adjacent residents worldwide. In response, many transportation agencies have adopted functional surface materials to reduce noise at the source on pavement, but assessing their effectiveness remains expensive and logistically challenging. Close Proximity (CPX) testing quantifies tire-pavement noise but requires specialized equipment costing $50,000-$126,000 and is limited to existing pavement, preventing proactive noise assessment during pavement design. This study develops machine learning models to predict CPX noise levels from readily available pavement characteristics, eliminating the need for costly tests during design and planning phases. To train and …

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. …

An Ensemble Learning-Based Approach To Quantify Post-Earthquake Functional Recovery Of A Steel Moment-Resisting Frame Inventory, Mohsen Zaker Esteghamati, Shiva Baddipalli

Civil and Environmental Engineering Faculty Publications

The quest for seismic resiliency requires designing for performance objectives beyond life safety. Functional recovery is an emerging objective often defined as the time required to restore a building’s basic functionality to the pre-event level. Nevertheless, quantifying functional recovery is a complex, computationally intensive process that is challenging to integrate into a standard design workflow. This study develops a machine learning (ML) model to map design and geometric features of steel special moment-resisting frames (SMRFs) to their functional recovery under two hazard levels: design-basis (DBE) and maximum considered (MCE) earthquakes. First, functional recovery time was quantified for an inventory of …

Computer Vision And Machine Learning Approaches For Defect Detection In 3d-Printed Cementitious Materials: A Systematic Review, Muhammad Ali Musarat, Ruben Paul Borg, Jingjie Wei, Carl James Debono, Kamal Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases, Web of Science and Scopus, were utilised for data extraction of …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Data-Driven Prediction Of Binder Rheological Performance In Rap/Ras-Containing Asphalt Mixtures, Eslam Deef-Allah, Magdy Abdelrahman

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt recycling technologies have advanced considerably over the last few decades with the utilization of reclaimed asphalt pavements (RAP) and recycled asphalt shingles (RAS). Characterizing aged and heterogeneous binders in these mixtures is challenging, particularly with limited extracted binders. This study suggests a data-driven framework that considers the rheological, chemical, and thermal characteristics to predict the binders' performance. Ninety-seven mixtures with 0–35% of the asphalt binder replaced with RAP/RAS binders were included as cores from the field, plant-produced mixtures, and laboratory-fabricated mixtures. The binders were chemically quantified using aging, aromatic, and aliphatic indices. Thermal analyses of the binders involved the …

Towards Advancing Streamflow And Peak Flow Prediction With Machine Learning: Identifying Infrastructure At Risk, Sudan Pokharel

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between …

Temporal Analysis Of Construction Safety Incidents In Southeastern U.S. Using Machine Learning Techniques, Mayowa O. Oladele

College of Graduate Studies: Theses & Dissertations

Construction safety incidents remain a significant concern, particularly in the Southeastern U.S. due to the high-risk nature of the industry. Analyzing patterns in these incidents can help improve safety practices and reduce accidents. Machine learning (ML) techniques were employed in this study to identify temporal patterns in construction safety incidents, aiming to enhance proactive safety management. The machine learning methods used in this research included logistic regression, decision trees, random forest, support vector machine (SVM), and K nearest neighbors (KNN).

The objective of the study was to analyze temporal trends in safety incidents and identify the most effective machine learningtechnique …

Advancing Data-Driven Disaster Debris Characterization And Quantification, Jasmine Bekkaye

LSU Doctoral Dissertations

Natural hazards generate tremendous amounts of debris that negatively impact communities and overwhelm waste management infrastructure. The challenge in disaster debris planning and management stems from inconsistent and scarce post-disaster debris quantity data due to the chaotic nature of recovery operations. This leads to a limited understanding of key factors influencing disaster debris generation across hazards and regions, hindering the accuracy and efficiency of modeling. With the increasing availability of comprehensive post-disaster waste datasets, the capacity to understand debris generation is expanding. This dissertation aims to fill knowledge gaps on disaster debris generation as follows: (1) investigate existing technologies for …

Identifying Waterway Traffic Flow Patterns Using Modified Clustering, Shihao Pang

Graduate Theses and Dissertations

Efficient management of inland waterways is essential for the economic and operational efficiency of transportation networks. Characterization and prediction of waterway vessel traffic flow patterns by time of day are critical for optimizing planned disruptive events like maintenance activities. This study identifies and predicts inland waterway traffic flow patterns along the Lower Mississippi River (LMR) using a modified clustering approach. A five-year period of Automatic Identification System (AIS) data, which tracks vessel movements in real-time, is used for model development and evaluation. The model first segments the river into approximately one-mile-long traffic message channels (TMCs) to estimate vessel counts and …

Real-Time Barge Detection Using Traffic Cameras And Deep Learning On Inland Waterways, Geoffery Eyram Agorku

Graduate Theses and Dissertations

Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels, e.g., barges. While methods to track vessels, e.g., tug and tow boats, are publicly available through Automatic Identification Systems (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are non-existent, especially in real-time settings. This paper develops a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models, specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet are employed. …

Development Of A Turning Movement Estimator, Somayeh Nazari Enjedani

Boise State University Theses and Dissertations

Turning Movement (TM) counts at intersections are crucial for several reasons. There has been extensive research on this topic throughout the history of traffic research, which reflects its importance in transportation engineering and urban planning. It can be said that one of the most significant applications of TMs is signal timing at intersections. Signal timing design requires data on the turning decisions of vehicles that travel through intersections to allow enough time for each movement at the intersection. Most of organizations around the USA are still applying traditional methods for collecting TM data, such as manual counts. These methods are …

Ai-Enabled Vibration Sensing System For Early Detection Of Trains At Active Highway-Rail Grade Crossings, Mohsen Amjadian, Md. Masnun Rahman, Constantine Tarawneh, Valik Villarreal, Dylan Rocha

Civil Engineering Faculty Publications

Highway-rail grade crossings (HRGCs) play an essential role in ensuring the secure traversal of road users across railway tracks. However, despite their significance, they present safety challenges, particularly when trains go undetected, heightening the risk of potential collisions between the road user and train. This paper aims to explore the viability of employing vibration sensors for detection and characterization of an approaching train’s speed at HRGCs. The methodology involves analyzing rail vibrations and developing a time series predictive machine learning (ML) model. To accomplish this, a Finite Element (FE) model of a ballasted track railway is created in SAP2000, consisting …

Forecasting Future Research Trends In The Construction Engineering And Management Domain Using Machine Learning And Social Network Analysis, Gasser G. Ali, Islam H. El-Adaway, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef

Civil Engineering Faculty Publications

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two …

Investigating Urban Impacts On Temperature And Rainfall Using Drone, Radar And Machine Learning Techniques, Junaid Ahmad

Civil Engineering Dissertations - Archive

The global urban population is increasing, and it is anticipated that approximately 70% of people will reside in urban areas by 2050. Urbanization changes land use and land cover, altering local climatology. For example, various urban centers across the globe are experiencing extreme rainfall events, resulting in widespread damage to life and property with possible linkages to urbanization. The use of artificial materials in urban areas brings significant changes to the surface temperatures. Due to the high heat capacity of most of the construction materials, the temperature of the urban area can increase substantially compared to the rural areas. This …

Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning, Syed Ibrahim Hassam

Doctoral

Regular inspections of pavements are conducted by civil infrastructure departments to evaluate the surface condition. Pavement surfaces are subject to deterioration caused by several factors such as traffic, weather, and sunlight. This deterioration becomes evident through various distresses, including potholes, rutting, cracking, bleeding, patching, and ravelling, which gradually affects the surface layer over time. It is essential to assess the condition of pavements as it not only ensures their usability but also maximises public safety. Effective pavement maintenance requires substantial resources and capital investment to carry out the most suitable maintenance treatments at the optimal time. Furthermore, the outcomes of …

Characterizing Technology Impacts On Driving Behaviors, Crash Risks, And Infrastructure Performances, Jobaidul Boni

Civil Engineering Dissertations - Archive

In recent years, an increase in driver distraction appears due to the rise in smartphone usage and the introduction of social media. Researchers put significant efforts in examining the impacts of distracted driving, mostly focused on distraction like texting or phone call. However, scant research exists to identify the underlying factors causing the distracted driving particularly caused by social media or showing their safety implications at complex geometries such as intersections and highways. This dissertation offers three independent studies reviewing the impact of technology on drivers' actions using field tests and simulation experiments. The first field tests conducted at three …

Risk Assessment Of Reinforced Concrete Sewer Pipes Under External Loading And Adverse Environmental Conditions Using An Adaptive Neuro-Fuzzy System, Khaled Saleh Khaled Abuhishmeh

Civil Engineering Dissertations - Archive

Failure of sewer mains poses a significant threat to the society, necessitating a robust risk assessment tool that integrates failure likelihood and associated consequences for effective prioritization of mitigation efforts. This dissertation addresses this need through three key objectives: 1. Failure Likelihood Assessment: The study utilizes Monte-Carlo simulation to evaluate the probability of sewer main failures in common agressive environments, considering factors like sulfide and chloride exposures. It highlights that chloride-induced cracks and bond strength loss are more critical than sulfide-induced wall thickness loss. The degradation of concrete and reinforcement properties under chloride attack significantly reduces ductility, emphasizing the importance …

Unraveling Water Quality Issues In The Colorado River Basin: Utilizing Remote Sensing Satellite Images, Statistical, And Machine Learning For Improved Monitoring, Godson Ebenezer Adjovu

UNLV Theses, Dissertations, Professional Papers, and Capstones

This research was aimed at exploring innovative and cost-effective tools in understanding the spatiotemporal variability of water quality parameters in the Colorado River Basin (CRB), which includes the Colorado River and major reservoirs and lakes in the USA including Lake Mead. The river which arises in the state of Colorado and empties into the Republic of Mexico at the Gulf of California, is a source of water to seven US states and the Republic of Mexico and provides water to about 40 million people and million acres of farmlands in seven states in the western US and the Republic of …

Comparison Of Condition Prediction Models To Prioritize Sewer Pipe Inspections, Madhuri Arjun

Civil Engineering Dissertations - Archive

Over time, wastewater collection systems deteriorate, necessitating ongoing adjustments and the development of asset management frameworks by utility proprietors to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition assessment for system-efficient operation and maintenance. In the United States, closed-circuit television (CCTV) is the most common method for inspecting the interior of sewer pipelines. This procedure is expensive and time-consuming due to a city's extensive inventory of pipes. Due to the immense quantity of these pipes, every municipality can only inspect some sections of sanitary sewer pipes promptly. Therefore, the …

Estimation Of Suspended Sediment Concentration Along The Lower Brazos River Using Satellite Imagery And Machine Learning, Trevor Stull

Civil Engineering Theses - Archive

ABSTRACT: Suspended sediment transport in river basins is important for many water management planning activities to maintain safe drinking water for the community and maintenance of water quality and waterways for the ecosystem. Currently, the traditional way to measure suspended sediment effectively and reliably is by collecting field samples in the river body, which is very time consuming and only provide a point value of suspended sediment within the waterbody at the instant the sample was taken. This thesis focuses on developing models that estimate suspended sediment concentrations for the lower Brazos River using satellite imagery from publicly available data …

Machine-Learning-Based Model For Hurricane Storm Surge Forecasting In The Lower Laguna Madre, Cesar E. Davila Hernandez, Jungseok Ho, Dong-Chul Kim, Abdoul Oubeidillah

Civil Engineering Faculty Publications

During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on …

Historical And Forecasted Kentucky Specific Slope Stability Analyses Using Remotely Retrieved Hydrologic And Geomorphologic Data, Daniel M. Francis

Theses and Dissertations--Civil Engineering

Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses.

This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil …

Soil Moisture And Geomorphologic Data For Use In Dynamic And Forecastable Landslide Hazard Analyses In Eastern Kentucky, Daniel M. Francis, L. Sebastian Bryson

Civil Engineering Research Data

These data are the geomorphologic and land information system-based soil moisture estimates from assimilation of NASA SMAP satellite-based observations and NOAH 3.6 Land Surface Model estimates over known landslides in Eastern Kentucky. Additionally Long Short-Term Memory Recurrent Neural Network and logistic regression machine learning codes, as well as an Application programming interface code are included. Finally, in-situ data from Eastern Kentucky is included.

Spatiotemporal Retrievals Of Soil Moisture And Geomorphologic Data For Landslide Sites In Eastern Kentucky, Lindsey Sebastian Bryson, Daniel M. Francis

Civil Engineering Research Data

These data are the soil texture, land information system-based soil moisture estimates from assimilation of NASA SMAP satellite-based observations and NOAH 3.6 Land Surface Model estimates, artificial neural network machine learning code, and in-situ soil moisture measurements.

Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson

College of Graduate Studies: Theses & Dissertations

The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak’s predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, …

A Computer Vision-Based Method For Tack Coat Coverage Inspection Using Drone-Collected Images, Aida Da Silva

Graduate Theses, Dissertations, and Problem Reports (ETD)

Tack coat is a thin asphalt applied between the existing surface and asphalt overlay during road rehabilitation. The uniformity of tack coat coverage plays a vital role in providing adhesive bonding between the two layers in the pavement structures. To ensure tack coat uniformity, the current practice primarily relies on manual inspection during construction by field experts. This process is time-consuming and tedious, and the results can be subjective and error-prone. Drones have emerged as a non-destructive sensing technology in the construction industry for many inspection practices. Unlike other non-destructive inspection technologies, drones offer benefits ranging from accelerating data collection …

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold …

Multimodal Imaging Of Structural Concrete Using Image Fusion And Deep Learning, Sina Mehdinia

Dissertations and Theses

Concrete structures may be exposed to a variety of loads and environments during their service life. Non-destructive testing (NDT) techniques can be helpful in evaluating the condition of a structure. Imaging provides a visual representation of the interior of concrete and its condition non-destructively. Ground penetrating radar (GPR) and ultrasonic echo array (UEA) using electromagnetic and stress waves, respectively, provide the data that can be used to reconstruct an image. In this PhD dissertation, image reconstruction and fusion algorithms, simulation, and a deep learning model were investigated with the goal to lay the foundation for enhanced imaging applications for concrete. …