GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.Comment: 58 pages, 20 figure

Evaluating the Impacts of Accelerated Incident Clearance Tools and Strategies by Harnessing the Power of Microscopic Traffic Simulation

Traffic incidents cause Americans delay, waste fuel, cause injuries, and create toxic emissions. Transportation professionals have implemented a variety of tools to manage these impacts and researchers have studied their effectiveness, illustrating a wide range between different tools and locations. To improve this state of knowledge, this dissertation sought to 1) identify prominent and effective incident management strategies, 2) model six selected incident management strategies within five highway corridors in South Carolina, and 3) apply benefit-cost analysis to evaluate the impact of various combinations of these strategies. To meet these objectives, the author evaluated published literature of the selected strategies, administered a nationwide survey of these strategies, conducted traffic simulation, and performed benefit-cost analysis. The literature review guided the author to fill gaps in knowledge regarding the effectiveness and expense of identified strategies. The nationwide survey identified effective incident management tools, the extent of their adoption, and their common problems. The author then applied PARAMICS traffic simulation software to evaluate the impact of six tools at five sites on metropolitan interstates throughout South Carolina. Finally, benefit-cost analysis was used to evaluate the benefits against costs at each study site. The survey provided many insights into both the effectiveness and collaboration within and among traffic incident management agencies and guided the author in selecting tools for evaluation. While the simulation study found that as the severity and duration of incident increases, so does the potential benefit of incident management tools, the frequency of incidents also produces significant impact on annual benefits. The benefit-cost analysis indicated that while all the incident management tools evaluated provided more benefits than costs, freeway service patrols and traffic cameras produced the highest return for incidents of varying severity. It was also found more advantageous to select one expensive but efficient incident management technology, rather than engage in the incremental deployment of various systems that might provide redundant benefits. Departments of transportation across the United States see the need to manage incidents more efficiently, consequently this dissertation developed data and analysis to compare benefits with costs to aid decision makers in selecting tools and strategies for future incident management endeavors

Application of Project Management Strategies and Tools for an Efficient and Successful Competition-based Engineering Senior Capstone Design Project

The industry-level engineering workforce for a project in modern times requires a clear plan and management process to execute the goals of the consumer and the producer. The engineers of tomorrow need the ability to be competitive and successful upon entry into the industry, where there have already been established management tactics for the execution of the company\u27s goals. The mentality within the industry is adaptable to senior collegiate-level competition-based capstone projects. Therefore the West Virginia University EcoCAR Mobility Challenge team has adapted, altered, or adjusted industry-level practices in order to have an overall functioning and effective team that follows a project management plan evaluating industry. The main intention of the EcoCAR Mobility Challenge is to convert a stock vehicle into an hybrid electric vehicle over four years following the Vehicle Development Process (VDP). The team started with fresh new members and team management at the start of the competition, and over the course of the competition, the team was able to adapt, alter and adjust industry-level management tactics and practices into the overall successful team. In Year 1 of the competition the team placed seventh and through the practice of using the tools from industry finished in third place in Year 3 of the competition. By executing a project management plan, teams at the university level can mitigate risk, develop proper schedules, team structures, communicate efficiently, and be successful. The skills adapted and used from industry for a competitive and efficient competition-based senior-level capstone not only will make the project itself successful as it would in industry, but knowledge of these tools prepares the students for the demanding rigorous career within a project-based or product-based industry of choice. The methods of management and tactics adopted by the team cover traditional and agile management, along with understanding management tactics in terms of communication, team structure and organization, scheduling, risk management, requirements management and change management. The tactics of management covered in this document can be adapted and applied to any engineering competition project with the desire to produce a successful product and manage and operate an efficient team for continued sustainability for future endeavors

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Proceedings of the First Karlsruhe Service Summit Workshop - Advances in Service Research, Karlsruhe, Germany, February 2015 (KIT Scientific Reports ; 7692)

Since April 2008 KSRI fosters interdisciplinary research in order to support and advance the progress in the service domain. KSRI brings together academia and industry while serving as a European research hub with respect to service science. For KSS2015 Research Workshop, we invited submissions of theoretical and empirical research dealing with the relevant topics in the context of services including energy, mobility, health care, social collaboration, and web technologies

Leveraging blockchain in chemical supply chain trading

This diploma thesis focuses on the integration of blockchain technology into supply chain trading. Real-world examples are examined to gain insights into the benefits and challenges of implementing blockchain in supply chain operations. Blockchain is a decentralized technology that utilizes cryptographic techniques and consensus mechanisms to establish a secure and transparent ledger of trans-actions. Each transaction is grouped into blocks and linked together using unique identifiers. Consensus mechanisms validate and add new blocks to the block-chain. This decentralized approach enhances data security, transparency, and immutability. The experimental work conducted in this diploma successfully implemented a digital twin and utilized cloud computing in a small-scale manufacturing facility. By integrating the Heroku cloud platform and establishing a digital twin connected to the cloud, the study demonstrated the potential for real-time monitoring, data analysis, and secure data management within the context of blockchain technology. Measurements were performed on a helical coil heat exchanger to assess its heat transfer efficiency, and a simplified 3D model was created for simulation. The simulation results were compared to the actual measured temperatures, showing a close correspondence with slightly lower temperatures consistently observed. The discussion highlights the practical benefits of digital twin and the potential integration of blockchain in the chemical engineering industry, while addressing the factors contributing to these minor deviations. These findings underscore the significance of leveraging blockchain, digital twin and cloud technologies to improve efficiency, sustainability, and safety in industrial processes. Further research and development in this area has the potential to drive significant advancements in the field of chemical engineering and manufacturing

Managing Water Resources in Large River Basins

Management of water resources in large rivers basins typically differs in important ways from management in smaller basins. While in smaller basins the focus of water resources management may be on project implementation, irrigation and drainage management, water use efficiency and flood operations; in larger basins, because of the greater complexity and competing interests, there is often a greater need for long-term strategic river basin planning across sectors and jurisdictions, and considering social, environmental, and economic outcomes. This puts a focus on sustainable development, including consumptive water use and non-consumptive water uses, such as inland navigation and hydropower. It also requires the consideration of hard or technical issues—data, modeling, infrastructure—as well as soft issues of governance, including legal frameworks, policies, institutions, and political economy. Rapidly evolving technologies could play a significant role in managing large basins. This Special Issue of Water traverses these hard and soft aspects of managing water resources in large river basins through a series of diverse case studies from across the globe that demonstrate recent advances in both technical and governance innovations in river basin management

Harnessing Hydro-kinetic Energy from Wake-Induced Vibration (WIV) of Bluff bodies

In this dissertation, the application Wake-Induced Vibration (WIV) of a bluff body for harnessing the kinetic energy of a fluid flow is presented. WIV arises when a body undergoes vibrations in the wake of an upstream body. This project investigates the WIV of a bluff body (circular cylinder), constrained to vibrate in the transverse direction, operating in the wake produced by a stationary and upstream bluff body. The upstream body serves as an energy concentrator and increases the oscillations experienced by the downstream body. An efficient coupling of the spatially and temporally concentrated energy from the upstream body and the downstream and vibrating body will result in WIV being considered as a viable form of renewable energy. The application of induced vibration due to vortices in harnessing hydrokinetic energy of the fluid is relatively immature and this research work, which is written as a compilation of journal articles, attempts to address major scientific and technological gaps in this field. The wake behind a bluff body augments the hydro-kinetic energy in space as well as time, in the form of a vortex street. Firstly, the kinetic energy distribution of a bluff body (circular cylinder) wake is characterized using numerical modelling, in order to identify the form and density of the available energy. Secondly, the spatial and temporal energy in the wake from different bluff bodies is investigated experimentally to identify a flow energy concentrator that is more suitable for WIV than the circular cylinder. The semicircular, straight-edged triangular, convex-edged triangular and trapezoidal cylinders were chosen for this analysis where the semicircular and convex-edged triangular cylinders were found to augment more temporal energy compared to the circular cylinder. Thirdly, experiments were performed in the water channel to investigate the effects of Reynolds number and separation gaps for the different cross-sections of upstream cylinders. The results indicated that an upstream semicircular cylinder produces more efficient WIV in a downstream circular cylinder compared to an upstream circular cylinder. In addition, both numerical and experimental results indicated that a staggered arrangement with 3 ≤ /D ≤ 4 and 1 ≤ /D ≤ 2 (here, D is the diameter of the cylinder, and x and y are the horizontal and vertical offsets, respectively) is the optimum arrangement among all test cases to harness the energy of vortices, resulting in a power coefficient of 33%. This was achieved due to the favourable phase lag between the velocity of the cylinder and force imposed by the fluid. Finally, the effect of mass and damping ratio of the downstream cylinder is investigated to optimize the vibration efficiency of the staggered semicircular-circular cylinder WIV system. The results of this test showed that a lower damping ratio results in lower impedance of the system and hence a larger vibration response. The vibration response was also inversely proportional to the mass ratio, however, a mass ratio of 2 – 3 proved to be the most efficient for the WIV system resulting in a maximum efficiency of 49%.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Application of a Blockchain Enabled Model in Disaster Aids Supply Network Resilience

The disaster area is a dynamic environment. The bottleneck in distributing the supplies may be from the damaged infrastructure or the unavailability of accurate information about the required amounts. The success of the disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a reliable dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts. This study analyzes the application of blockchain technology on disaster management resilience. The influences of this most promising application on the disaster aid supply network resilience combined with the Internet of Things (IoT) and Dynamic Voltage Frequency Scaling (DVFS) algorithm are explored employing a network-based simulation. The theoretical analysis reveals an advancement in disaster-aids supply network strategies using smart contracts for collaborations. The simulation study indicates an enhance in resilience by improvement in collaboration and communication due to more time-efficient processing for disaster supply management. From the investigations, insights have been derived for researchers in the field and the managers interested in practical implementation