期刊
ENERGY AND BUILDINGS
卷 165, 期 -, 页码 92-105出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2018.01.051
关键词
LCA; Health impact; Environment; Conservation works; Repair; Stone; Laser; Poultice; Water cleaning; Solvents
Historic buildings constitute a large amount of the European building stock and their preservation is of paramount importance for cultural, economic and also environmental reasons, as their conservation is regarded as more sustainable with respect to demolition and reconstruction. However, historic buildings require frequent repair and conservation works, whose environmental impact is still to be evaluated. In the present paper the sustainability of conservation intervention is evaluated by the LCA analysis method, focusing in particular on cleaning materials and technologies, with reference to conservation sites located in Bologna, Italy. After identifying the most used cleaning materials and technologies, the time necessary for achieving an equal cleaning effectiveness was selected as functional unit and the LCA analysis was carried out, along with a quantitative evaluation of some other key aspects of the different methods (workers' health, acoustic impact and waste produced in the building site). The results highlight that the different technologies involve remarkably different environmental impacts, but that in some cases their impact could be easily reduced by substituting some secondary materials in the cleaning process, without affecting the effectiveness of cleaning. The results also show that the methods involving the lowest environmental impact are not necessarily the best ones in terms of safety and waste production in the building site. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
Article
Construction & Building Technology
Cool roof strategies for urban thermal resilience to extreme heatwaves in tropical cities
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Towards a blockchain and machine learning-based framework for decentralised energy management
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Dual-layer optimization design method for collaborative benefits of renewable energy systems in building clusters: Case study of campus buildings
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Model predictive control of distributed energy resources in residential buildings considering forecast uncertainties
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Comprehensive assessment of double skin façades: A mathematical model for evaluating influence of KL ratio on electrical and thermal performances, and indoor conditions
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS (2024)
Article
Construction & Building Technology
Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS (2024)
分享论文
