Abstract
A vital area of study is cost estimation in building engineering. Traditional models' poor capacity to appropriately capture the changing trend of construction engineering costs leads to low precision in cost forecast for construction engineering. The result is the recommendation of a descriptive data mining-based cost prediction model for construction engineering. The engineering cost database is initially constructed using a number of distinctive indices, and the aberrant data are discovered and filtered using the K-means clustering method. Second, the mathematical model is created, and the LSSVM is used to solve it. The model is then optimized using the PSO method. According to the experimental findings, this model's prediction accuracy is good and its average root-mean-square error (RMSE) for 100 samples is 2.27-e. The estimated value of individual cost in this model is the most accurate when compared to other models, and the predicted difference is just 30 Yuan/m2. Because it can estimate engineering costs with greater accuracy and stability, this finding has important implications for engineering investment choices.
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References
Ali ZH, Burhan AM, Kassim M, Al Khafaji Z (2022) Developing an integrative data intelligence model for construction cost estimation. Complexity. https://doi.org/10.1155/2022/4285328
Chen Y (2022). Research on IGOA-LSSVM based fault diagnosis of power transformers. J Vibroeng. https://doi.org/10.21595/JVE.2022.22439
Cui L (2022) Research on cost prediction of civil engineering construction based on multi-dimensional data mining. Int J New Dev Eng Soc. https://doi.org/10.25236/IJNDES.2022.060107
Ding C, Ding Q, Feng L, Wang Z (2022) Prediction model of dissolved gas in transformer oil based on VMD-SMA-LSSVM. IEEJ Trans Electr Electron Eng. https://doi.org/10.1002/TEE.23653
Dmitrieva TL, Kablukov AV (2021) Implementation of an optimization algorithm using modified Lagrange functions on the example of a steel hinge-rod system. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/751/1/012061
Jiang Y (2021) Application of data mining technology in field verification of project cost. Adv Multimedia. https://doi.org/10.1155/2021/3585878
Jianqing L, Chenchen W, Liming C (2022) Transmission engineering cost prediction based on data mining. Int J Front Eng Technol. https://doi.org/10.25236/IJFET.2022.040101
Jo HG, Lee D (2021) East Asian herbal medicine for cancer pain: a protocol for systematic review and meta-analysis with using association rule analysis to identify core herb pattern. Medicine. https://doi.org/10.1097/md.0000000000027699
Ksenija T, Diana C-P, Marija Š (2019) Cost estimation in road construction using artificial neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04443-y
Liu G, Wang L, Liu D, Fei L, Yang J (2022) Hyperspectral image classification based on non-parallel support vector machine. Remote Sens. https://doi.org/10.3390/RS14102447
Ma L, Wang Q, Liao S, Guo T, Xiong Y, Ming Y, Zou Y (2021) Research on cost prediction of power transmission and transformation project based on combination prediction model. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/769/4/042019
Miao F, Ashutosh S (2021) Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0. Int J Intell Comput Cybernet. https://doi.org/10.1108/IJICC-10-2020-0142
Muhamediyeva DT (2022) Building and training a fuzzy neural model of data mining tasks. J Phys Confer Ser. https://doi.org/10.1088/1742-6596/2182/1/012024
Pham TD, Nguyenthihong D, Vovan T (2022) Improving the ANFIS forecating model for time series based on the fuzzy cluster analysis algorithm. Int J Fuzzy Syst Appl. https://doi.org/10.4018/IJFSA.313602
Shen Y, Ma N, Men Y, Zhao X, Jia Z (2020).Research on overhead line Engineering Cost prediction based on PCA-LSSVM model. E3S Web Conf. https://doi.org/10.1051/e3sconf/202018502023
Sun H, Chen Y, Lai J, Wang Y, Liu X (2021) Identifying tourists and locals by K-means clustering method from mobile phone signaling data. J Transp Eng Part A Syst. https://doi.org/10.1061/JTEPBS.0000580
Wang B, Dai J (2019) Discussion on the prediction of engineering cost based on improved BP neural network algorithm. J Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-179193
Wu Y (2022) Research on optimization of construction project cost control in construction enterprises. Academic J Bus Manag. https://doi.org/10.25236/AJBM.2022.041312
Xu X, Peng L, Ji Z, Zheng S, Tian Z, Geng S (2021) Research on substation project cost prediction based on sparrow search algorithm optimized BP neural network. Sustainability. https://doi.org/10.3390/SU132413746
Yang D (2022) An improved particle swarm optimization algorithm for parameter optimization. Comput Inform Mech Syst. https://doi.org/10.12250/JPCIAMS2022090508
Yang H, Xie B, Liu X, Chu X, Ruan J, Luo Y, Yue J (2022) Breakdown pressure prediction of tight sandstone horizontal wells based on the mechanism model and multiple linear regression model. Energ. https://doi.org/10.3390/EN15196944
Yun S (2022) Performance analysis of construction cost prediction using neural network for multioutput regression. Appl Sci. https://doi.org/10.3390/APP12199592
Zhang R, Ma R (2022) Non-invasive load identification method based on ABC-SVM algorithm and transient feature. Energy Rep. https://doi.org/10.1016/J.EGYR.2022.10.075
Zhao S (2021) Using artificial neural network and WebGL to algorithmically optimize window wall ratios of high-rise office buildings. J Comput Des Eng. https://doi.org/10.1093/JCDE/QWAB005
Acknowledgements
We would like to thank Research Center for Value Evaluation and Creation of Private Enterprises and Audit Value Innovation Research Team in the New Era which contribute to this study greatly.
Funding
The work was supported by the Construction Plan of Scientific Research and Innovation Platform of Wuhan College, the project number is KYP202001 and supported by the Research and Innovation Team of Wuhan College, the project number is KYT201903.
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DK contribution lies in the design, study conception and the writing of the first draft; JD contribution lies in data collection and analysis and Material preparation. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Ke, D., Dai, J. Multidimensional analysis of engineering cost database based on descriptive data mining. Soft Comput (2023). https://doi.org/10.1007/s00500-023-07992-6
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DOI: https://doi.org/10.1007/s00500-023-07992-6