Environmental Hazards, Год журнала: 2024, Номер unknown, С. 1 - 26
Опубликована: Сен. 22, 2024
Язык: Английский
Transportation Research Part D Transport and Environment, Год журнала: 2025, Номер unknown, С. 104592 - 104592
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
6
Cleaner Materials, Год журнала: 2024, Номер 12, С. 100242 - 100242
Опубликована: Март 26, 2024
This work examines the environmental impact of low-carbon concrete that incorporates supplementary cementitious materials (SCMs). After reviewing near-zero carbon SCMs and concrete, a life cycle assessment (LCA) was undertaken for mix designs with normal-to-high compressive strengths, incorporating limestone fly ash as cement replacements. The analysis includes relevant region-specific inventory parameters raw materials, energy production, transportation. A comparative between embodied emissions material mechanical performance is then made. results this paper indicate in can reduce emissions, yet at proportional decrease properties compared to conventional concrete. combination produced, on average, higher strength by 20.5% lower CO2-eq values 21.1% when blends. associated transportation main constituents production were average below 4% total per mix. In addition eco-mechanical quantitative assessments, study offers insights recommendations development considering global resource availability constituents.
Язык: Английский
Процитировано
14
Ocean & Coastal Management, Год журнала: 2024, Номер 258, С. 107399 - 107399
Опубликована: Окт. 2, 2024
Язык: Английский
Процитировано
13
BIO Web of Conferences, Год журнала: 2024, Номер 86, С. 01058 - 01058
Опубликована: Янв. 1, 2024
In the context of Industry 5.0, this long-term study assesses significant influence AI-based sustainability metrics. It also illuminates a novel paradigm in which artificial intelligence (AI) and human expertise work together to jointly drive sustainability, financial performance, employee satisfaction, overall ecological responsibility. AI-driven efforts produced surprising 12% reduction trash creation, an amazing 7% energy usage, 8% drop CO2 emissions over five-year period. Financially speaking, these actions showed up as steady 4% annual revenue growth, $2 million cost reductions on average each year, cumulative 3.4% gain return investment. The factor is even more notable, with satisfaction ratings rising from 4.2 4.7 work-life balance scores significantly 4.1 4.6. By 2024, 70% workers will have adopted AI, demonstrating how essential AI has become working. An all-encompassing score that included dynamic components increased 60 75 indicating general improvement sustainability. This emphasizes mutually beneficial relationship between 5.0. shows fosters sustainable balanced industrial future by improving environmental responsibility workforce while producing benefits.
Язык: Английский
Процитировано
12
Transportation Research Part D Transport and Environment, Год журнала: 2024, Номер 127, С. 104072 - 104072
Опубликована: Янв. 19, 2024
Climate change impacts such as extreme temperatures, snow and ice, flooding, sea level rise posed significant threats to railway infrastructure networks. One of the important questions that managers need answer is, "How will maintenance costs be affected due climate in different scenarios?" This paper proposes an approach estimate implication on life cycle cost (LCC) railways assets. The proportional hazard model is employed capture dynamic effects reliability parameters LCC A use-case from a North Sweden analyzed validate proposed process using data collected over 18 years. results have shown precipitation, temperature, humidity are weather factors selected use-case. Furthermore, our analyses show under future scenarios about 11 % higher than without impacts.
Язык: Английский
Процитировано
12
Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 148, С. 105776 - 105776
Опубликована: Апрель 22, 2024
Alongside the transition towards sustainable and renewable infrastructure energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on Architecture, Engineering Construction (AEC) industry, with shift focus cost carbon management across lifecycle through use of digitalisation techniques sustainability rating systems. This paper aims develop systematic framework enhance underground early design construction stages, aiming for "build clever efficiently". integrates digitalisation, assessment standards, numerical modelling, optimisation assess emissions establish benchmarks products processes. The approach uses parametric models define tunnelling-related entities such as geological formations, TBM, segments, designed dimensional constraints allow adaptive modelling interactions. Enabled by building information (BIM) enriched domain properties, module-based accounting is adopted evaluate footprint material- asset-level key emitters functional units. Leveraging established databases, baselines segmental lining designs reduction strategies are established. Through project-driven case studies, correlations among geotechnical conditions, dimensions, TBM operating parameters developed quantify emissions. A prototype programme demonstrates framework's application typical metro road tunnels at different levels details (LoD). serves guideline conducting embodied assessments specifically tailored tunnelling, acknowledging substantial contribution process, which controlled conditions.
Язык: Английский
Процитировано
12
Engineering Structures, Год журнала: 2024, Номер 320, С. 118887 - 118887
Опубликована: Сен. 4, 2024
Процитировано
8
Energy and AI, Год журнала: 2024, Номер 16, С. 100351 - 100351
Опубликована: Фев. 18, 2024
Power system is vital to modern societies, while it susceptible hazard events. Thus, analyzing resilience characteristics of power important. The standard model infrastructure resilience, the triangle, has been primary way characterizing and quantifying in systems for more than two decades. However, theoretical provides a one-size-fits-all framework all specifies general curves (e.g., residual performance duration recovery). Little empirical work done delineate curve archetypes their fundamental properties based on observational data. Most existing studies examine analytical models constructed upon simulated performance. There dire dearth field, which hindered our ability fully understand predict systems. To address this gap, study examined hundred power-grid related outages three major extreme weather events United States. Through use unsupervised machine learning, we different archetypes, as well each archetype. results show grid curves, triangular trapezoidal curves. Triangular characterize behavior properties: 1. critical functionality threshold, 2. recovery rate, 3. pivot point. Trapezoidal explain sustained function loss constant rate. longer loss, slower rate recovery. findings provide novel perspectives enabling better understanding prediction
Язык: Английский
Процитировано
7
Results in Engineering, Год журнала: 2024, Номер unknown, С. 103834 - 103834
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
7
Transportation Research Part D Transport and Environment, Год журнала: 2024, Номер 133, С. 104229 - 104229
Опубликована: Май 23, 2024
Язык: Английский
Процитировано
5