Keyword search (4,164 papers available)

"uncertainty" Keyword-tagged Publications:

Title Authors PubMed ID
1 Adaptive sliding mode fault-tolerant control of an over-actuated hybrid VTOL fixed-wing UAV under transition flight Wang B; Zhao H; Hu X; Shen Y; Li N; 41475926
ENCS
2 Intolerance of uncertainty, psychological symptoms, and pain in long-term childhood cancer survivors: a report from the Childhood Cancer Survivor Study Alberts NM; Stratton KL; Leisenring WM; Pizzo A; Lamoureux É; Alschuler K; Flynn J; Krull KR; Jibb LA; Nathan PC; Olgin JE; Stinson JN; Armstrong GT; 40699439
PSYCHOLOGY
3 Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks Adcock B; Brugiapaglia S; Dexter N; Moraga S; 39454372
MATHSTATS
4 Exploring the effects of anthropogenic disturbance on predator inspection activity in Trinidadian guppies Brusseau AJP; Feyten LEA; Crane AL; Brown GE; 38476138
BIOLOGY
5 Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration Hou D; Zhan D; Wang L; Hassan IG; Sezer N; 37936825
ENCS
6 How uncertainty affects information search among consumers: a curvilinear perspective He S; Rucker DD; 36471868
JMSB
7 UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection Abdar M; Salari S; Qahremani S; Lam HK; Karray F; Hussain S; Khosravi A; Acharya UR; Makarenkov V; Nahavandi S; 36217534
ENCS
8 Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis Zhu Y; Chen Z; 36191500
ENCS
9 Viral Anxiety Mediates the Influence of Intolerance of Uncertainty on Adherence to Physical Distancing Among Healthcare Workers in COVID-19 Pandemic Chung S; Lee T; Hong Y; Ahmed O; Silva WAD; Gouin JP; 35733798
PSYCHOLOGY
10 Decision-first modeling should guide decision making for emerging risks Morgan K; Collier ZA; Gilmore E; Schmitt K; 35104915
ENCS
11 Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY; 34388923
CHEMBIOCHEM
12 Assessing the regional biogenic methanol emission from spring wheat during the growing season: A Canadian case study Cai M; An C; Guy C; Lu C; Mafakheri F; 34182392
ENCS
13 A robust optimization model for tactical capacity planning in an outpatient setting Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D; 33215335
ENCS
14 Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography-tandem mass spectrometry method Camirand Lemyre F; Desharnais B; Laquerre J; Morel MA; Côté C; Mireault P; Skinner CD; 32476284
CHEMBIOCHEM
15 Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China. Hao J, Chen Z, Zhang Z, Loehlein G 32358748
ENCS
16 An ecological framework of neophobia: from cells to organisms to populations. Crane AL, Brown GE, Chivers DP, Ferrari MCO 31599483
BIOLOGY
17 Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited 'FAO estimate' of 25. Eskola M, Kos G, Elliott CT, Hajšlová J, Mayar S, Krska R 31478403
CHEMBIOCHEM
18 Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction. Vorwerk J, Aydin Ü, Wolters CH, Butson CR 31231178
PERFORM

 

Title:Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China.
Authors:Hao JChen ZZhang ZLoehlein G
Link:https://www.ncbi.nlm.nih.gov/pubmed/32358748?dopt=Abstract
DOI:10.1007/s11356-020-09026-2
Publication:Environmental science and pollution research international
Keywords:ChinaConstruction waste reductionPrefabricationQuantifyingUncertainty analysisWeight and volume
PMID:32358748 Category:Environ Sci Pollut Res Int Date Added:2020-05-03
Dept Affiliation: ENCS
1 Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou Industrial Park, Suzhou, 215123, China. Jianli.hao@xjtlu.edu.cn.
2 Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada.
3 Anhui Construction Engineering Real Estate Co. Ltd, Huaibei, Anhui, China.
4 Department of Architecture, Xi'an Jiaotong-Liverpool University, Suzhou, China.

Description:

Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China.

Environ Sci Pollut Res Int. 2020 May 01;:

Authors: Hao J, Chen Z, Zhang Z, Loehlein G

Abstract

Due to the rapid pace of urbanization in China, there has been a significant increase in construction work, which has resulted in the generation of more waste. Reducing the waste at source is the most efficient way to reduce its negative impacts, and prefabrication is a construction method that does exactly that. Since prefabricated construction generates less waste compared to conventional cast-in-situ construction, it is being promoted by the Chinese government. This study investigates the benefits of prefabrication and quantifies the percentage of construction waste reduction through its application in China. It does so by using a 26-storey concrete-brick residential building as a case study, and by conducting uncertainty analysis with Oracle Crystal Ball simulation software to assess the reduction of waste when using prefabricated components in place of cast-in-situ elements. Simulation results demonstrated that the waste generation rate for in-situ timber formwork and masonry work was 10.52 and 4.77 kg/m2 respectively, and that the use of prefabricated components reduced those figures by 36.04% and 25.53% respectively. This study quantifies the benefits of prefabrication as a method for reducing the generation of construction waste in China. Not only would extensive use of prefabrication decrease the cost related to construction waste management in China, but it could also mitigate the environmental and social impacts of construction waste globally.

PMID: 32358748 [PubMed - as supplied by publisher]





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