Version 1
: Received: 18 May 2024 / Approved: 20 May 2024 / Online: 20 May 2024 (09:47:04 CEST)
How to cite:
Al-Sabah, B.; Anbarjafari, G. Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks. Preprints2024, 2024051265. https://doi.org/10.20944/preprints202405.1265.v1
Al-Sabah, B.; Anbarjafari, G. Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks. Preprints 2024, 2024051265. https://doi.org/10.20944/preprints202405.1265.v1
Al-Sabah, B.; Anbarjafari, G. Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks. Preprints2024, 2024051265. https://doi.org/10.20944/preprints202405.1265.v1
APA Style
Al-Sabah, B., & Anbarjafari, G. (2024). Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks. Preprints. https://doi.org/10.20944/preprints202405.1265.v1
Chicago/Turabian Style
Al-Sabah, B. and Gholamreza Anbarjafari. 2024 "Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks" Preprints. https://doi.org/10.20944/preprints202405.1265.v1
Abstract
In the ambitiously evolving construction industry of Kuwait, characterized by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. This research paper introduces a groundbreaking deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This meticulous analysis allows for the detection of deviations that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Additionally, our findings proactively sketch the future market trajectory and facilitate anomaly detection, thereby contributing substantially to the academic discussion on integrating machine learning into construction management. This research offers valuable insights for professionals within Kuwait, aiming to harness deep learning techniques to boost operational efficiency and strategic foresight in the construction sector.
Keywords
Autoencoder Neural Networks; Anomaly Detection in Construction Data; Machine Learning in Building Management; Construction Market Analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
