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A Pilot Study on ChatGPT’s Potential in Developing Construction Education Assessments Through a Lens of Bloom’s Taxonomy
Get PDFEffective and quality assessment is crucial in construction management (CM) education to evaluate students’ understanding of the diverse skills demanded by the industry. Integrated by the American Council for Construction Education (ACCE) guidelines for its accredited CM programs, Bloom\u27s Taxonomy provides a systematic and structured framework for aligning learning objectives with cognitive processes, guiding educators in developing assessments. However, under real-world practice, crafting quality assessment questions that align with Bloom\u27s Taxonomy and the ACCE student learning outcomes (SLOs) poses challenges for CM instructors due to the specialized expertise and complexity of knowledge. This study investigates the potential of ChatGPT, an LLM-based chatbot, to assist educators in generating assessment questions specific to construction education with the integration of Bloom\u27s Taxonomy. Focusing on two specific SLOs: 1) create construction project cost estimates, and 2) analyze methods, materials, and equipment to construct projects. The study adopted the latest ChatGPT o1-preview model for question generation (QG). These questions were then evaluated by the researchers based on criteria and alignment with Bloom\u27s Taxonomy, clarity, sufficiency of information, feasibility, and relevance to learning objectives. Findings revealed that while ChatGPT can produce relevant and cognitively appropriate questions related to the two SLOs, several limitations still exist regarding clarity and completeness of information. The study concludes that ChatGPT is a potent tool to support CM educators in developing assessment materials but requires proper prompt engineering and additional context to enhance question quality. Future research should involve more data collection methods and a tailored database for ChatGPT training
A Prediction Model for Greenhouse Gas Emissions and Patterns across Boundary Scopes in the Construction Phase – Utilizing Explainable Machine Learning
No full textThe recent intensification of global warming, often referred to as global boiling due to climate change, has emerged as a critical societal challenge, accelerating global efforts to achieve carbon neutrality through greenhouse gas (GHG) reduction initiatives. South Korea has set a target to achieve carbon neutrality by 2050 and aims to reduce GHG emissions by 40% compared to 2018 levels by 2030. Consequently, the construction industry is also under increasing pressure to develop sustainable and effective strategies for reducing GHG emissions. However, existing GHG management systems in construction often fail to adequately account for project-specific characteristics, limiting their capability to accurately predict and manage the total volume and patterns of emissions. This study proposes a model capable of precisely predicting GHG emissions and their patterns across boundary scopes (Scopes 1,2, and 3) during the construction phase. The proposed model utilizes a Case-Based Reasoning (CBR) methodology, incorporating a filtering engine based on multiple machine learning models to achieve both explainability and high predictive accuracy. The average prediction accuracy (APA) of the proposed model demonstrated excellent performance, with Scope 1 at 80.2%, Scope 2 at 92.8%, and Scope 3 at 80.0%. The proposed model supports government agencies in establishing rational emission allowances tailored to project characteristics and assists construction firms in identifying GHG emission risks at an early stage, enabling the development of effective mitigation strategies. The proposed model is anticipated to become a practical tool for GHG management in the construction sector, making a significant contribution to the industry\u27s carbon neutrality objectives
Vacancy to Vibrancy – External Effects, Finance Models and Collaboration in Heritage Building Revitalization. Framework
Get PDFVacant buildings can negatively impact a city\u27s social and economic appeal. Addressing this issue is crucial, and the revitalization of these buildings becomes imperative. Revitalizations often require large investments, especially when it comes to listed buildings. Listed buildings are significant buildings that are legally protected to preserve their cultural heritage. Therefore these listed buildings must be revitalized in a sensitive way to preserve their unique character. This leads to discussions wether this high level of effort and costs is in relation to the building\u27s long-term impact. At this point, it is sensible to incorporate external socio-economic effects such as the Bilbao effect, where a building can lead to increased tourism in a city. This highlights the need for collaboration between different stakeholders, leveraging both the resources of the private sector while ensuring public interests.
This study begins with a literature review of socio-economic external effects of revitalizations, followed by an exploration of financing models that link these external effects with the necessary investments. The study concludes with a framework, designed to promote collaboration between public and private stakeholders
Analysis of Social Value Creation in Infrastructure Delivery to Achieve the Sustainable Development Goal 3
No full textGood health and well-being are key indicators and a precondition for sustainable development, allowing people to enjoy fulfilling lives, receive education and be productive members of society. Social value is the positive impact of infrastructure projects on long-term well-being. Infrastructure social value (SV) delivery is a practical national/organisation-level vehicle for realising the United Nations\u27 Sustainable Development Goal Three - ensuring healthy lives and promoting well-being for all ages. However, infrastructure SV delivery is often commissioned when the project is well underway or completed, so mechanisms to collect the required data to quantify SV impact were not set up. By that point, it is usually late. Qualitative and quantitative methodologies are applied using the Goodison Legacy Project (GLP) as a case study. Twenty-five interviews were conducted to analyse the SV commitments of the GLP. A quantitative formula was developed for the definition and estimation of SV components to indicate the well-being status of the end-users before the infrastructure project commences and possible evaluation of infrastructure impact on the community. A framework is developed for the GLP to demonstrate the SV delivery of infrastructure projects to realise SDG 3. The study emphasised that evaluating the well-being status of potential project users before the infrastructure project commences plays a central role in indicating where to invest and maximise resources. Thus, it helps monitor how construction projects impact society\u27s well-being, economic changes, and the built environment. The approach explored could revolutionise how to evaluate effective SV delivery of infrastructure to realise the SDGs
A Review of the State-of-the-Art of Rainscreen Cladding Performance in Residential Building Walls
No full textThis paper presents a state-of-the-art review of rainscreen cladding performance in residential building walls, with a focus on their moisture management capabilities, integration with windows, and use of exterior insulation to enhance building durability and energy efficiency. Rainscreens effectively control moisture and protect buildings from the weather, but challenges exist, particularly in integrating windows without disrupting the wall system\u27s thermal and moisture performance, cost of rainscreen walls, and maintenance need. To address these challenges, this study involved two main approaches: (1) conducting an in-depth review of existing research, industry resources, and key documents from the Rainscreen Association in North America (RAINA), focusing on topics such as moisture management, airflow challenges around windows, rainscreen systems with continuous insulation, types of rainscreen cladding, cost analysis, installation challenges, techniques for installation above and below windows, and maintenance requirements; and (2) consulting industry experts to gain their professional insights and perspectives. A cost analysis of rainscreen systems reveals that while prefabricated solutions are more expensive than traditional façade systems, economies of scale may reduce costs over time. Industry insights suggest that while the use of rainscreens with ci can improve long-term building performance, challenges such as installation complexity, damage to weather barriers, and the need for ongoing maintenance must be considered for the long-term performance of rainscreen walls. By combining research findings and industry insights, this paper provides valuable guidance for architects, engineers, and builders in optimizing rainscreen systems in residential for better moisture management, durability, and energy efficiency in building envelope systems
SAM-based Segmentation of Multi-Class Bridge Components from Diverse Real-Scene Inspection Images
No full textTraditional bridge inspection methods rely on manual visual inspection, which is time-consuming, labor-intensive, and potentially dangerous. Automated inspection approaches, which use unmanned aerial vehicles (UAVs) and computer vision, aim to address this issue. However, three knowledge gaps remain. First, although considerable research has been conducted on defect detection and segmentation in bridge inspection, there has been limited focus on segmenting and characterizing specific bridge components that contain defects. Such segmentation provides essential contextual information for understanding the importance of defects for maintenance decision making. Second, existing bridge component recognition approaches face challenges in generalizing across various scenarios, especially in close-range inspections where contextual information is often missing. Third, current developments in the foundation models in the computer vision, such as the segment anything model (SAM), remain unexplored for bridge component segmentation from inspection images due to its lack of domain-specific knowledge and unable to assign semantic labels to multiple segmented components. To address these limitations, this paper proposes a SAM-based image segmentation method for multi-class bridge component segmentation from diverse bridge inspection images. This method leverages the SAM architecture and pre-training from Segment Anything 1 Billion (SA-1B) to enhance feature extraction and improve generalizability. The method also integrates a U-Net decoder to address the challenges of multi-class bridge component segmentation. The proposed method was trained and tested end-to-end on seven classes based on the FHWA’s Bridge Inspector’s Reference Manual. The results demonstrate promising performance, indicating the potential of this SAM-based approach for efficient and accurate bridge component segmentation
Comparative Analysis of BERT and RoBERTa for Construction Site Incident Report Classification
No full textMost studies that investigated the automated classification of construction incident reports relied on traditional machine learning approaches, line support vector machines, decision trees, and logistic regression. Those approaches, however, have limited capacity to capture rich contextual text information to inform predictions. This study, therefore, investigates the effectiveness of two transformer-based models, BERT and RoBERTa, in classifying construction site incident reports. The purpose of this research is to evaluate the performance of these models in capturing contextual and semantic nuances in incident reports and to determine which model is better suited for supporting safety management tasks in the construction industry. A comparative framework was employed, where both models were fine-tuned on a dataset of construction site incident reports obtained from the United States Occupational Safety and Health Administration (OSHA). The results show that while both models achieved high accuracy, precision, and recall, RoBERTa demonstrated superiority in capturing more relevant context to inform its predictions. Specifically, RoBERTa outperformed BERT in classifying incidents related to Caught in/between objects and showed marginal improvements in other categories. The findings of this research have implications for construction companies seeking to automate site incident report analysis for timely decision-making and provide insights for researchers deciding between BERT and RoBERTa for classification tasks. This study contributes to developing more accurate and reliable safety management systems in the construction industry by adopting transformer-based models to automate the analysis of incident reports
Development of the change readiness diagnostic tool through industry 4.0 transformation; UK infrastructure sector case study
Get PDFIndustry 4.0 implementation still proves to be a challenge within the UK infrastructure sector and the decision-makers within organisations in the sector despite its clear importance as it introduces several changes to the organisation\u27s structure, processes, and business practices. Due to the challenges and lack of understanding in the sector, it is vital for change management to be highly considered in the decision-making process of implementation of Industry 4.0 initiatives to ensure successful implementation. It has been highlighted that organisational culture and resistance to change is a key barrier to successful implementation which should be addressed before organisations implement Industry 4.0 initiatives. The development of a readiness tool has been proposed to assess organisations\u27 readiness to implement Industry 4.0 initiatives before implementation. The evaluation of readiness for organisations allows the decision-makers to bridge the gaps if they can on the changes and challenges that are presented by Industry 4.0 initiatives. The methodology for this study was undertaken using an inductive approach where case studies were observed, and semi structures interviews were undertaken. The findings from the analysis highlighted six key aspects to consider assessing readiness for prior to industry 4.0 strategies implementation in the infrastructure sector. They are (1) Organisations need, (2) Organisations Willingness to Change, (3) Employees Willingness to Change, (4) Support from Management, (5) Organisation Productiveness, and (6) Organisations Willingness to Invest. This study demonstrates a readiness tool which can allow organisations to assess their readiness prior to the adoption of industry 4.0 strategies
A Conceptual Framework For Implementing Safe Structural Options and Risk Assessments in the Alteration of Structures - A Case Study of Ross House, UK
Get PDFStructural alteration and refurbishment continue to account for a sizeable portion of the UK Construction Industry projects. These projects range from simple domestic extensions and alterations to very complex multi-storey building modifications and refurbishment. The more complex the project, the higher the risk to health and safety of the construction personnel. Residual risks (where risk elimination fails) may also be passed on to the users of the completed project. To mitigate this, feasibility of the proposals from an architectural standpoint must be subjected to a rigorous process of existing foundation and structural investigations aimed at generating results that will ensure that proposed solutions can be safely implemented throughout the lifecycle of the project. This research work used a case study approach to design a framework which facilitates a wholistic process of technical options and risk assessment. The procedure is also proposed as a means of integrating emerging digital technologies which are currently being used to carry out different parts of the investigative and design process. The case study building (known as Ross House) is an existing 3 storey Reinforced Concrete (RC) framed office building. It is proposed to add 2 storeys at roof level and convert the entire building into residential usage. Investigations conducted included a foundation/ structural frame assessment and structural analysis (3D modelling) for both existing and proposed conditions. The procedure was then made into a flowchart that facilitates a comprehensive approach to technical solutions and risk management. Further work is recommended to validate the procedure on other similar projects and to incorporate digital analytical processes into the finalised framework
Construction Delay Disputes: a Model for a Reduction in the Escalation of Technical and Factual Disagreements
Get PDFAlthough construction delays are often inevitable, the escalation of construction delay claims is problematic as it is a leading and persistent cause of costly disputes. Such claims involve disagreements over legal, technical and factual matters. Disputed delay assessments are at the core of the latter. This research involves the analysis, synthesis, and evaluation of academic and professional literature, and twenty-one case studies involving construction, infrastructure, and engineering projects located on three continents. While the literature has been highly critical of the current approaches to the measurement and management of construction delays, it has not offered a comprehensive solution to this issue. This paper finds that a reduction of construction delay disputes can be achieved by using a model of work based upon the use of (i) reliable source materials; (ii) information repositories and technologies to produce, validate, and share such source materials, and (iii) mechanisms that increase contractual certainty, namely delay analysis protocols. Such a model of work offers an advanced solution that is likely to increase agreements on the impact of delays during the contract administration phase of construction projects and thus reduce the current rate of escalation of claims into disputes