A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air quality-Reference-Cited by-同舟云学术

A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air quality

Published:2022-01 Issue: Volume:207 Page:108533
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Li Lu^ORCID,Zhang Yumiao^ORCID,Fung Jimmy C.H.^ORCID,Qu Huamin,Lau Alexis K.H.^ORCID

Funder

HSBC Bank USA

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference50 articles.

1. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants;Klepeis;J. Expo. Sci. Environ. Epidemiol.,2001

2. Prediction of indoor temperature in an institutional building;Afroz;Energy Procedia,2017

3. Personal exposure to indoor aerosols as actual concern: perceived indoor and outdoor air quality, and health performances;Oh;Build. Environ.,2019

4. Improving indoor air quality, health and performance within environments where people live, travel, learn and work;Kelly;Atmos. Environ.,2019

5. Continuous weeklong measurements of personal exposures and indoor concentrations of fine particles for 37 health-impaired North Carolina residents for up to four seasons;Wallace;Atmos. Environ.,2006

Cited by 37 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mechanically ventilated classrooms in central Italy's heritage school buildings: Proposal of archetypes and CO2 prediction models;Building and Environment;2024-11

2. Development of a mixed chamber experimental and CFD database of indoor environments;Building and Environment;2024-10

3. Neural network based self-tuning IPID for wave glider heading tracking control;Ocean Engineering;2024-09

4. Prediction of stratified ground consolidation via a physics‐informed neural network utilizing short‐term excess pore water pressure monitoring data;Computer-Aided Civil and Infrastructure Engineering;2024-08-17

5. A stacking machine learning model for predicting pullout capacity of small ground anchors;AI in Civil Engineering;2024-07-30