Economic Cost Prediction Model for Building Construction Based on CNN-DAE Algorithm
Abstract
This paper aims to explore the effectiveness of constructing the construction economic cost prediction model of construction engineering by using CNN-DAE algorithm combined with CNN-DAE. As the construction cost of construction engineering is affected by a variety of complex factors and has high nonlinear and dynamic changes, traditional prediction methods are often difficult to achieve ideal prediction accuracy. Therefore, this paper proposes an innovative predictive model to address these challenges. The final experimental results show that the construction economic cost prediction model based on CNN-DAE algorithm can more accurately capture the change trend of construction cost in terms of prediction accuracy, and provide strong decision support for project managers. In addition, the model has strong generalization ability, and can adapt to different scales and types of construction projects.References
UK Lilhore, S Simaiya, S Dalal, N Faujdar, YK Sharma, KBVB Rao, et al. ProtienCNN‐BLSTM: An efficient deep neural network with amino acid embedding‐based model of protein sequence classification and biological analysis. Computational Intelligence, 2024, 40(4): e12696-e12696.
MA Rahim, MM Rahman, MS Islam, AJM Muzahid, MA Rahman, D Ramasamy, Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit. Expert Systems with Applications, 2024, 125080.
NA El-Hag, HM El-Hoseny, F Harby. DNN-driven hybrid denoising: advancements in speckle noise reduction. Journal of Optics, 2024, 1-10.
BZ Cheng, M Wang, N Gao, H Hou. Machine learning inversion design and application verification of a broadband acoustic filtering structure. Applied Acoustics, 2022, 187: 108522.
D Ding, K Li, Y Li, G Jin. A tunable diode laser absorption spectroscopy (TDLAS) signal denoising method based on LSTM-DAE. Optics Communications, 2024, 130327.
W Sun, Y Zhang, F Chen. Research on heart and lung sound separation method based on DAE–NMF–VMD. EURASIP Journal on Advances in Signal Processing, 2024, 2024(1): 59.
BAA Demiss, WA Elsaigh. Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties. Engineering Research Express, 2024, 6(3): 032102.
S Zhang, Y Zheng, X Li, H Ye, L Dong, X Huang, et al. A novel solar flare forecast model with deep convolution neural network and one-against-rest approach. Advances in Space Research, 2024, 74(7): 3467-3480.
N Jaroenchai, S Wang, LV Stanislawski, E Shavers, Z Jiang, V Sagan, et al. Transfer learning with convolutional neural networks for hydrological streamline delineation. Environmental Modelling and Software, 2024, 181: 106165.
M Lin, X Chen, G Chen, Z Zhao, D Bassir. Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks. Stochastic Environmental Research and Risk Assessment, 2024, 1-17.
J Zhu, J Yan, J Zhang, L Yu, A Song, Z Zheng, et al. Automatic segmentation of high-risk clinical target volume and organs at risk in brachytherapy of cervical cancer with a convolutional neural network. Cancer/Radiotherapie, 2024.
M Zheng, X Luo. Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models. International Journal of Electrochemical Science, 2024, 19(9): 100747.
L Mei, S Jin, T Huang, H Peng, W Zha, J He, et al. High-accuracy and high-throughput reactive lymphocyte identification using lightweight neural networks. Biomedical Signal Processing and Control, 2024, 97: 106722.
AA Elgohary, MM Badr, NA Elmalhy, RA Hamdy, S Ahmed, AA Mordi. Transfer of learning in convolutional neural networks for thermal image classification in Electrical Transformer Rooms. Alexandria Engineering Journal, 2024, 105: 423-436.
BI Anghel, R Lupu. Understanding Regulatory Changes: Deep Learning in Sustainable Finance and Banking. Journal of Risk and Financial Management, 2024, 17(7): 295.
Chang, S Yang, S Li, X Gu. Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder. Measurement Science and Technology, 2024, 35(9): 096007.
S Song, X Zhao, Z Zhang, M Luo. A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder. Sensors (Basel, Switzerland), 2024, 24(12): 4006.
F Xu, Z Hao, C Zhou, Y Deng. Bearing condition monitoring via an unsupervised and enhanced stacked auto-encoder. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024, 46(6): 367.
PR Kumar, B Shilpa, RK Jha, BD Raju, TK Mohammed. Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network. Sādhanā, 2024,4 9(2): 1-13.
H Byeon, GC Prashant, SA Hannan, FY Alghayadh, A. M. Soomar, , M. Soni, , et al. Deep Neural Network Model for Enhancing Disease Prediction using Auto Encoder based Broad Learning. SLAS technology, 2024, 29(3): 100145.
U Otokwala, A Petrovski, H Kalutarage. Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. International Journal of Information Security, 2024, 23(4): 2559-2581.
C Lai, P Baraldi, E Zio. Physics-Informed deep Autoencoder for fault detection in New-Design systems. Mechanical Systems and Signal Processing, 2024, 215: 111420.
S An, J Shi, R Liu, Y Chen, J Wang, S Hu, et al. scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and dirichlet process mixture model. Bioinformatics, 2024, 40(4): btae198.
WY Kang, YL Gao, Y Wang, F Li, JX Liu. KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder. IEEE journal of biomedical and health informatics, 2024, 28(5): 3178-3185.
DOI:
https://doi.org/10.31449/inf.v49i5.7029Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
