bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reducing the CO2 Footprint of UHPC Through Portland Cement Substitution
Authors: Lande Ingrid , Sørensen Andrej A. , Hagen Martin , Thorstensen Rein Terje 
Published: January 2025

AbstractThis study focuses on reducing the CO2-eq footprint of ultra-high-performance concrete (UHPC) by substituting Portland cement with alternative binders. Three types of cement types which are pre-accepted in the standard EN 197 were used: CEM I with >95% ordinary Portland cement (OPC), CEM II with 30% ground-granulated blast-furnace slag (GGBS), and CEM II with 70% GGBS. Fresh state consistency, compressive strength, and flexural tensile strength were evaluated, in addition to a comparison of CO2-eq footprints. Results showed that while the compressive strength was brought to the same level for all three mixes when curing at elevated temperatures for a short period, significant reductions in CO2-eq emissions were achieved: up to 60% for UHPC excluding steel fibers and 40% when including 2 vol.% micro steel fibers. The study also highlighted the potential for further improving strength properties by reducing water content in mixes with higher GGBS content. Overall, this research underscores the potential to reduce the environmental impact of UHPC through incorporating alternative materials in UHPC production that are already pre-accepted for industrial use.
The Use of Electrodeposition Technology to Promote Self-Repairing of Cracks in Concrete: A Mini Review
Authors: Alzard Mohammed H. , El-Hassan Hilal 
Published: January 2025

AbstractConcrete, hailed for its widespread utility in construction, faces intrinsic challenges, primarily its vulnerability to cracking and subsequent degradation. This chapter reviews the evolving landscape of self-healing concrete strategies, spotlighting autogenous, and autonomous healing methodologies while focusing on the emerging electrodeposition technology. The exploration distinguishes autogenous self-repair, which relies on natural processes like continuous hydration and calcium carbonate formation, from autonomous approaches, which employ engineered additives for immediate crack intervention. Detailed insights into autogenous healing mechanisms unravel the role of continuous hydration and calcium carbonate formation in sealing microcracks, albeit with inconsistent outcomes. Limitations in achieving consistent autogenous healing have propelled research into autonomous methods. Notably, electrodeposition technology emerges as a frontrunner, utilizing controlled currents and engineered compounds to fill cracks and shield the concrete from deterioration. This chapter emphasizes electrodeposition’s efficacy, showcasing studies demonstrating its ability to seal cracks of varying widths by depositing compounds like ZnO, Mg(OH)2, and CaCO3. Parameters like current density and immersion solutions significantly influence its efficiency. While promising, the universal application of electrodeposition remains under research, urging further exploration to refine parameters, broaden applicability, and bolster reinforced concrete’s durability. This review encapsulates critical facets of self-healing concrete, particularly highlighting electrodeposition’s potential and ongoing challenges. It aims to pave the way for enhanced concrete resilience and sustainability in construction practices.
Predicting Dry Shrinkage Using Machine Learning Methods
Authors: Khodabandeh Peyman , Azarhomayun Fazel , Shekarchi Mohammad , Kioumarsi Mahdi 
Published: January 2025

AbstractModeling drying shrinkage presents significant challenges due to the complexity and multitude of contributing parameters. This study provides detailed insights into the input requirements and predictive capabilities of established models by leveraging various datasets from the NU-ITI database. Initially, the performance of a shrinkage model was evaluated. The data for a machine learning random forest model included eight variables, interpreted through SHapley Additive exPlanations (SHAP), which elucidates the most influential inputs. However, the partial dependency graphs yielded minimal information on their relative impacts. This research demonstrates that enhancements in the random forest model’s predictive accuracy improved shrinkage predictions by 25%. This advancement significantly mitigates potential deviations in anticipated strains and stresses. The findings from this comprehensive analysis facilitate the selection and prediction of drying shrinkage, focusing on the most critical factors to ensure the highest accuracy.
Upcycling of Single-Use Pallet Wood to Cross-Laminated Timber
Authors: Vonk Niels H. , Niederwestberg Jan , Oorschot Ron W. A. , de Jong Jan 
Published: January 2025

AbstractTo reduce the emission of greenhouse gases within the building industry, effort must be put into reducing waste and converting reclaimed materials towards reuse. Within the Netherlands, discarded wood is incinerated for energy generation, releasing the carbon that has been captured during growth. To extend the lifetime and the carbon storage duration and, consequently, improve the environmental performance of “wood waste,” re-use is desired. Hence, the production and structural performance of cross-laminated timber (CLT) made of current wood waste were explored in this study. Single-use pallets were collected and manually disassembled with all fasteners removed. The wood was then finger-jointed, edge-glued, planed, and fabricated into 1.2 × 3 m CLT. In total eight CLT panels were made with different layups, of which three were used in this work. Mechanical testing indicated the potential of the created elements, showing similar performances to new CLT. While the bending tests suffered due to some manufacturing problems, planar shear tests showed good performances, similar to new CLT.
Evaluation of the Compressive Strength of Fly Ash- Based Geopolymer Concrete Using Machine Learning
Authors: Bypour Maryam , Yekrangnia Mohammad , Kioumarsi Mahdi 
Published: January 2025

AbstractThe advancement of geopolymer concrete technology presents a sustainable solution to address the challenges associated with the significant carbon footprint of the construction industry. This research uses machine learning methods to predict the compressive strength of fly ash-based geopolymer concrete using a wide range of experimental results. Furthermore, the effect of different features, including the components of fly ash-based geopolymer concrete, alkaline activator, and other additives, was investigated utilizing Shapley values technique. The results show that both XGBoost and linear regression models can predict the compressive strength of the studied dataset with acceptable accuracy. In XGBoost approach, R2 values for the train and test data were obtained 0.98 and 0.86, respectively, which leads to more accurate results than linear regression method. Additionally, based on the results, MgO is regarded as the most influential factor in compressive strength compared to other investigated features. Furthermore, the concentration of the alkaline activator, NaOH, positively impacts the target value.
Decarbonizing Conventional Building Materials for Net-Zero Emissions: A Feasibility Study in Canada
Authors: Dara Chi 
Published: January 2025

AbstractGovernments around the world are aiming for net-zero carbon emissions by 2050. To drive broader decarbonization efforts, the building industry is challenged by the mission to reduce embodied carbon emissions, which stem from building materials and systems. Most efforts in buildings are focused on operational energy, but up to 80% of a building’s emissions occur before use and occupancy, from extraction to construction phases. These emissions are irreversible and contribute around 11% of global carbon emissions. This study emphasizes the need to prioritize the decarbonization of conventional building materials during production using low carbon constituents to mitigate associated environmental impact. Quantitative data analysis and reviews have identified alternative low-carbon options for possible application in mix design. Examples include substituting general use (GU) Portland cement with Portland limestone cement, using supplementary cementitious materials (SCMs) to reduce cement content, and utilizing low-carbon concrete masonry units. Achieving net-zero embodied carbon requires promoting circular bio-based products and reducing conventional materials’ carbon throughout their life cycle. At material level analysis, this is possible not only by promoting the use of circular bio-based materials but also by purposefully reducing the embodied carbon of conventional building materials throughout their life cycle, as well as communicating the best practices as lessons learnt to promote broader adoption by the architecture, construction and engineering (ACE) industry. However, collaboration among designers, contractors, and manufacturers is essential. This study provides a preliminary pathway to overall decarbonization efforts, understanding that conventional, existing building materials will play a significant role in attaining the net-zero commitments of the future built environment.
Advancing Sustainable Construction Materials: Wood and Rubber Geopolymer Masonry Mix Development
Authors: Gigar Firesenay Zerabruk , Khennane Amar , Liow Jong-leng , Al-Deen Safat , Tekle Biruk Hailu , Fitzgerald Cooper J. , Basaglia Anthony , Webster Charlie 
Published: January 2025

AbstractRecycling industrial waste into construction materials is becoming a fundamental strategy, offering a hopeful path toward sustainable construction practices. This study focuses on the innovative reuse of end-of-service wood and crumb rubber to develop environmentally favorable materials. Their high availability, lightweight properties, and high-energy absorption capacity make them highly suitable as additives in masonry unit production. Furthermore, using them with sustainable binding material, such as geopolymer, enhances the overall sustainability of the masonry, facilitating rapid strength development and enhancing durability while providing increased protection against fire and weathering. The study involved the development of an optimal mix design, which can potentially be used for the production of load-bearing and non-load-bearing masonry units. This was achieved by examining various proportions of wood, as well as combinations of wood and rubber, using a partial–factorial experimental design. The results show that wood-to-binder ratios ranging from 0.2 to 0.4 can potentially be used for the production of wood–geopolymer masonry units. Additionally, a ratio of 0.3 (with 50% wood and 50% rubber) was identified as potentially suitable for producing wood and rubber-based units.
Machine Learning Integration in LCA: Addressing Data Deficiencies in Embodied Carbon Assessment
Authors: Hu Ming , Wang Chaoli , Ghorbany Siavash , Yao Siyuan , Nouri Ali 
Published: January 2025

AbstractLife Cycle Assessment (LCA) is an essential tool for quantifying the environmental burdens of products and processes, critical for advancing sustainability goals. Central to the effectiveness of LCA is the Life Cycle Inventory (LCI) phase, which requires reliable data to reflect the environmental footprint of products accurately. However, LCA practitioners often encounter data gaps that can compromise the assessment’s accuracy. To address this, we explore the integration of Machine Learning (ML) to enhance LCA data quality, particularly in the LCI stages B to D, which focus on product use, end-of-life, and beyond-life phases. This chapter introduces a novel framework that leverages ML to overcome LCI data challenges, emphasizing reducing the embodied carbon of construction products. We extract existing data from the Environment Product Declaration online library and apply natural language processing to interpret this unstructured data. Subsequently, we employ a random forest algorithm, a robust ensemble tree-based ML method, to refine the data analysis. We present a pilot study that validates the feasibility of our ML-enhanced framework. The incorporation of ML addresses the voluminous data in LCA. It augments the analytical capacity, thereby improving the precision and reliability of both LCI and Life Cycle Impact Assessment (LCIA) datasets. Consequently, our approach yields higher quality LCA outcomes, offering a more reliable basis for environmental impact evaluation. In summary, the successful application of ML in this research bridges the critical data gap in LCI for construction products, paving the way for a more sustainable industry through improved accuracy in environmental impact assessments and more informed decision-making in green product innovation.
Smart Technologies: New Perspectives for the Heritage Environment
Authors: Calzolari Marta , Davoli Pietromaria , Frighi Valentina 
Published: January 2025

AbstractToday, the role of the construction sector is fundamental to mitigating climate change, especially given the potential of innovations introduced during the Industrial Revolution. The Internet of Things (IoT), together with Internet-enabling technologies, has developed and spread even in the architecture, engineering, and construction (AEC) sector, though with some differences in purposes and objectives. However, especially in the field of heritage buildings, a gap in the definition of cultural heritage (CH) and the one in the concept of smartness need to be bridged. This chapter aims to identify specific innovative approaches for CH, through an analysis of a literature review and best practices related to the different applications of the aforementioned technologies, to develop a roadmap toward new perspectives against climate change while increasing the smartness of the heritage environment.
REVERT Framework: Stakeholder Perspective to Enable Circular Transformation of Construction Industry
Authors: Bostancı Hafize Büşra , Tanyer Ali Murat , Habert Guillaume 
Published: January 2025

AbstractThe stakeholders in various industries focus on developing innovative preventions to increase environmental, economic, and social sustainability to tackle the negative impacts of climate change. Hence, the interest in transitioning from a linear to a circular model has gained momentum in the last decade. The circular economy (CE), therefore, has attracted the construction industry stakeholders to adopt the sustainable model based on the CE principles because it is the most resource-consuming and waste-producing industry. Recycling the materials and construction and demolition waste, reusing the building components, renovating and refurbishing the buildings, adapting circular building design principles, innovating for a regenerative built environment, developing circular supply chains, and enabling circular business models are the core topics to ground this new model. However, the more perspectives, the more challenging the transition. Therefore, circular transition in the construction industry becomes compelling since it has complex and distributed collaborations due to its nature. Although the number of studies has increased, the circular construction industry from the stakeholder perspective still needs to be explored. Within this point of view, this study aims to present a new framework developed to increase stakeholder collaboration for circular transition at micro, meso, and macro-scales. It approaches the micro-scale for materials, meso-scale for buildings, and macro-scale for cities. Besides, it considers the construction industry target groups determined by the European Commission to demonstrate the circular building design principles. The research obtains data by literature review, maps the findings at micro, meso, and macro-scales, and employs natural language processing (NLP) to analyze the CE strategies. Followingly, it constructs thirty-seven success factors to treat the strategies obtained by NLP. Consequently, the REVERT framework based on six dimensions and thirty-seven success factors is introduced to consider stakeholder collaboration for circular transformation in the construction industry.
Urban Waste Recycling: A Multi-objective Approach for Sustainable Construction in European Regions
Authors: Komkova Anastasija , Krog Agergaard Sophie , Holt Andersen Birgitte , Habert Guillaume 
Published: January 2025

AbstractUrgent needs to mitigate climate change and reduce waste generation require collaborations between sectors. We examine the impacts of the potential recycling of ashes from the incineration of urban waste streams such as municipal solid waste, sewage sludge, and biomass within the construction sector. We perform material flow analysis to estimate the flows and stocks of ashes to identify underutilized waste streams. In this study, we use a multi-objective optimization model at the regional level (country level) to estimate optimal scenarios for urban waste recycling in the construction sector, with a focus on minimizing CO2 eq. emissions and costs. We combine geographic information systems and life cycle assessment (LCA) to examine the configurations of optimal supply chain networks, including direct symbiosis between waste producers and construction material manufacturers, as well as centralized waste recycling plants with recovery of valuable substances. We apply the proposed methodology to a case of sewage sludge recycling and phosphorus recovery in Denmark. Results show that recycling of sewage sludge ashes as supplementary cementitious materials with mineral recovery can bring up to 69% lower CO2 eq. emissions compared to business-as-usual ash landfilling and production of conventional cement construction materials and fertilizers. The methodology can be further applied to assess impacts with potential urban waste recycling in different regions. The results can provide indications to policymakers to encourage the establishment of new urban-industrial collaborations, promote sustainable waste management practices, and advance the goals of a circular economy.
Sustainability and Uncertainty in Collaborative Project Delivery
Authors: Narum Kristoffer Brattegard , Kals Per Morten , Lædre Ola 
Published: January 2025

AbstractBuilding construction has a profound impact on economic, social and environmental sustainability. The transformation of building construction towards reaching the United Nations sustainability goals will necessitate a transformation of project delivery models (PDMs) in construction projects. Successful management of uncertainty is essential for a project to reach its goals. Sustainable transformation is increasing the complexity and need for innovation in construction projects, and complex projects have a relatively high tendency to fail in reaching their goals. Collaborative project delivery models (CDMs) are considered particularly suitable for complex projects with high uncertainty and need for innovation. A CDM can be divided into contractual, organizational and cultural elements. The relationship between CDM elements, uncertainty and sustainability is insufficiently understood. This chapter seeks to fill the research gap by examining experiences from five large European collaborative projects studied in two longitudinal multiple-case studies carried out in cooperation with two of the largest project owners in the Norwegian construction industry. Experiences from the five case projects demonstrate how CDM elements facilitate early and joint management of opportunities and risks in a way which can advance both economic, social and environmental sustainability. For a CDM to successfully contribute to the advancement of sustainability, its elements must be selected and implemented in ways that ensure the recruitment, incentivization and organization of suppliers and personnel with competence and commitment to create a collaborative culture aimed at achieving sustainability goals. Social, political and market uncertainty in the front-end greatly impacts the ability of the client to select and implement such a CDM. In future research, it is recommended to study in detail how collaboration towards sustainability goals can be integrated into CDM elements and to carry out comparative quantitative studies on how projects with various forms of such integration perform economically, socially and environmentally.
Future-Proofing Energy Infrastructure: Power Grid Risk Assessment
Authors: Qudaisat Muneer , Houssou Dela , Gallus William , Alipour Alice 
Published: January 2025

AbstractClimate-change-imposed challenges in the form of heightened frequency and intensity of weather events exert additional pressure on securing the imperative continuous and reliable power supply, leading to increased power outages. This research proposes a comprehensive framework for enhancing the resilience of electric power networks (EPNs) through reliability-based risk assessment, promoting predictions and proactive decisions. The presented research discusses weather phenomena, their association with climate change, and their projected impacts. The numerical weather prediction model, WRF 3.4.1, with a 4 km resolution cell grid, gives a more accurate projection of high winds’ frequency and intensity. The simulation period from 2086 to 2099 is based on a reference control period spanning from 2000 to 2013, with adjustments made to background conditions using climate model output consistent with projections for the late century, a pseudo-global warming (PGW) technique. The presented research focuses on the wooden power distribution poles. The reliability assessment approach employs fragility development and analysis against wind scenarios through advanced modeling techniques and statistical analysis used to mimic historical and projected wind scenarios and to allow numerous factors on both the demand and capacity sides and their inherent uncertainties to be considered. The annual probability of failure is obtained by performing a mathematical convolution of the fragility and the hazard curves, showing the reflection of the effects of climate change on the annual probability of failure. Scaling these results to a system-level resilience assessment will facilitate the flexible energy design strategies integration and allow smoother net-zero standards incorporation and adaptation to the changing environmental conditions. This understanding will allow the decision-makers to evaluate the critical locations within a distribution line and plan to address the vulnerabilities by hardening the assets or implementing modern microgrid techniques or distributed energy resource integration.