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Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework (2024)

Hou, Fangli ; Ma, Jun ; Cheng, Jack C. P. ; [et al.]

Firenze University Press

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Publication Date: 2024-04-04

Description: Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios

Keywords: Early failure detection ; Abnormal data reconstruction ; Variational autoencoder (VAE) ; Long short-term memory network (LSTM) ; Sustainable IAQ management ; thema EDItEUR::U Computing and Information Technology

Language: English

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Chapter Integrating ESG Factors into Construction Projects: A Blockchain-Based Data Management Approach (2024)

Gong, Xingbo ; Das, Moumita ; Tao, Xingyu ; [et al.]

Firenze University Press

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Publication Date: 2024-04-08

Description: Environmental, Social, and Governance (ESG) investing has become increasingly significant in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry faces challenges such as non-uniform standards, complex information sources, and data security concerns when collecting and verifying ESG data. At the same time, as one of the key points of carbon emission in AEC projects, the ESG management of construction projects is still lacking. This paper proposed a blockchain-based ESG data management framework, which designed to address these challenges in the AEC industry. The framework and the smart contract and transaction data model applied in it realize data collection and information verification in construction projects. By leveraging blockchain technology's key features of transparency, immutability, and traceability, the framework ensures secure and efficient ESG data management. Additionally, the InterPlanetary File System (IPFS) technology enables access to original files for data verification and comparison, further enhancing authenticity. By integrating blockchain and IPFS technologies, our proposed solution enhances the reliability and traceability of ESG data in the construction projects, paving the way for more sustainable and transparent practices

Keywords: AEC ; Blockchain ; Construction Project ; ESG ; IPFS ; Smart contract ; thema EDItEUR::U Computing and Information Technology

Language: English

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