School of Management (SoM)

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Browsing School of Management (SoM) by Subject "33 Built Environment and Design"

Now showing 1 - 5 of 5
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    Comparative life-cycle assessment of novel steel section design with wire arc additive manufacturing
    (Springer, 2025-02-22) Arrè, Lidiana; Pagone, Emanuele; Laghi, Vittoria; Martina, Filomeno; Palermo, Michele; Lazou, Adamantia; Meskers, Christina; Olivetti, Elsa; Diaz, Fabian; Gökelma, Mertol
    Additive manufacturing, particularly Wire Arc Additive Manufacturing (WAAM), is emerging as a promising technology in the construction sector due to its potential to reduce environmental impacts. Life-Cycle Assessment (LCA) is a crucial methodology for evaluating the environmental footprint of products and processes that can be carried out from raw material extraction to the end of production, commonly referred to as “cradle-to-gate” analysis. This study focuses on the environmental impact of 3D-printed steel elements using WAAM technology for construction applications. Specifically, the conventional production of Circular Hollow Section (CHS) steel components was compared with the innovative production of Tubular Sandwich Section (TSS) steel components using WAAM. The analysis provides a comparison of the carbon footprint associated to both production methods, highlighting in detail the emission factors associated with each step of the WAAM production. The results highlighted that WAAM not only offers design and structural benefits to build complex-shaped geometries but also contributes to more sustainable construction practices with a lower “cradle-to-gate” carbon footprint due to the reduced material consumption associated with material efficiency.
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    Machine learning application to disaster damage repair cost modelling of residential buildings
    (Taylor and Francis, 2025) Wanigarathna, Nadeeshani; Xie, Ying; Henjewele, Christian; Morga, Mariantonietta; Jones, Keith
    Restoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.
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    Managing sudden unexpected disruptions in complex projects: the antifragility hierarchy
    (Taylor and Francis, 2024-12-31) Usher, Greg; Cantarelli, Chantal C; Davis, Kate; Pinto, Jeffrey K; Turner, Neil
    Projects are prone to a variety of sudden unexpected disruptions across their development cycle, requiring that effective organizations develop strategies for proactively recognizing disruption likelihood and swiftly responding to these events. This paper explores a hierarchy of responses to disruption, based on Taleb’s theory of antifragile system behavior. Following this reasoning, we suggest that when faced with project disruptions, organizations need to investigate the means to trigger a ‘convex’ response that increases value through antifragile thinking. We propose an ‘antifragility hierarchy’ in which three key responses to project disruption are demonstrated, with a range of strategies available for addressing these disruptions. This hierarchy offers a novel conceptualization of responses to project disruption events, suggesting that the options available to organizations range from robust (the least effective) to antifragile (the most constructive). Finally, we offer a set of strategies for effectively responding to disruptions to promote antifragility in projects.
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    Rapid decarbonization requires industrial efficiency
    (Springer, 2025-01-31) Yang, Miying; Evans, Steve
    The potential of effciency to support decarbonization is underestimated and overlooked relative to more expensive and intensive actions. Implementing resource and energy effciency strategies in industry could deliver rapid and cost-effective decarbonization.
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    Risk assessment for digital transformation projects in construction Enterprises: an enhanced FMEA model
    (Elsevier, 2025-05-15) Li, Tangzhenhao; You, Jianxin; Aktas, Emel; Dong, Yongxin; Yang, Miying
    The digital transformation of the construction industry is crucial for advancing global digital economies, but it involves significant risks that require a standardized and robust assessment methodology. This paper presents an enhanced Failure Mode and Effect Analysis (FMEA) model that integrates the Multiple Attribute Border Approximation Area Comparison (MABAC) method with Grey Relational Analysis (GRA). Unlike previous approaches, this integration aligns grey relational changes with border approximation vector components, capturing both positive and negative correlations between modes. This enhances the prioritization process by distinguishing failure modes that may amplify or mitigate each other’s impact, leading to more precise risk assessments and mitigation strategies. The model also employs interval numbers instead of crisp numbers to reduce information loss from decision-making ambiguities caused by heterogeneous expert evaluations. Applied in a real-life case study, the improved model effectively accommodates biases and hesitations in expert decision-making, enhancing the accuracy and reliability of risk assessments in digital transformation projects. The findings highlight the model’s potential as a comprehensive and reliable framework for identifying, prioritizing, and mitigating risks in the digital transformation of the construction industry.