Predicting construction costs often involves disadvantages, such as low prediction accuracy, poor promotion value and unfavorable efficiency, owing to the complex composition of construction projects, a large number of personnel, long working periods and high levels of uncertainty. To address these concerns, a prediction index system and a prediction model were developed. First, the factors influencing construction cost were first identified, a prediction index system including 14 secondary indexes was constructed and the methods of obtaining data were presented elaborately. A prediction model based on the Random Forest (RF) algorithm was then constructed. Bird Swarm Algorithm (BSA) was used to optimize RF parameters and thereby avoid the effect of the random selection of RF parameters on prediction accuracy. Finally, the engineering data of a construction company in Xinyu, China were selected as a case study. The case study showed that the maximum relative error of the proposed model was only 1.24%, which met the requirements of engineering practice. For the selected cases, the minimum prediction index system that met the requirement of prediction accuracy included 11 secondary indexes. Compared with classical metaheuristic optimization algorithms (Particle Swarm Optimization, Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, Differential Evolution and Artificial Fish School), BSA could more quickly determine the optimal combination of calculation parameters, on average. Compared with the classical and latest forecasting methods (Back Propagation Neural Network, Support Vector Machines, Stacked Auto-Encoders and Extreme Learning Machine), the proposed model exhibited higher forecasting accuracy and efficiency. The prediction model proposed in this study could better support the prediction of construction cost, and the prediction results provided a basis for optimizing the cost management of construction projects.
Citation: Zhishan Zheng, Lin Zhou, Han Wu, Lihong Zhou. Construction cost prediction system based on Random Forest optimized by the Bird Swarm Algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15044-15074. doi: 10.3934/mbe.2023674
Predicting construction costs often involves disadvantages, such as low prediction accuracy, poor promotion value and unfavorable efficiency, owing to the complex composition of construction projects, a large number of personnel, long working periods and high levels of uncertainty. To address these concerns, a prediction index system and a prediction model were developed. First, the factors influencing construction cost were first identified, a prediction index system including 14 secondary indexes was constructed and the methods of obtaining data were presented elaborately. A prediction model based on the Random Forest (RF) algorithm was then constructed. Bird Swarm Algorithm (BSA) was used to optimize RF parameters and thereby avoid the effect of the random selection of RF parameters on prediction accuracy. Finally, the engineering data of a construction company in Xinyu, China were selected as a case study. The case study showed that the maximum relative error of the proposed model was only 1.24%, which met the requirements of engineering practice. For the selected cases, the minimum prediction index system that met the requirement of prediction accuracy included 11 secondary indexes. Compared with classical metaheuristic optimization algorithms (Particle Swarm Optimization, Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, Differential Evolution and Artificial Fish School), BSA could more quickly determine the optimal combination of calculation parameters, on average. Compared with the classical and latest forecasting methods (Back Propagation Neural Network, Support Vector Machines, Stacked Auto-Encoders and Extreme Learning Machine), the proposed model exhibited higher forecasting accuracy and efficiency. The prediction model proposed in this study could better support the prediction of construction cost, and the prediction results provided a basis for optimizing the cost management of construction projects.
| [1] |
L. F. Cabeza, L. Rincon, V. Vilarino, G. Perez, A. Castell, Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: a review, Renewable Sustainable Energy Rev., 29 (2014), 394–416. https://doi.org/10.1016/j.rser.2013.08.037 doi: 10.1016/j.rser.2013.08.037
|
| [2] |
M. Y, Cheng, H. C. Tsai, E. Sudjono, Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry, Expert Syst. Appl., 37 (2010), 4224–4231. https://doi.org/10.1016/j.eswa.2009.11.080 doi: 10.1016/j.eswa.2009.11.080
|
| [3] |
A. Mahdavian, A. Shojaei, M. Salem, J. S. Yuan, A. A. Oloufa, Data-driven predictive modeling of highway construction cost items, J. Constr. Eng. Manage., 147 (2021), 04020180. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001991 doi: 10.1061/(ASCE)CO.1943-7862.0001991
|
| [4] |
A. Mahmoodzadeh, H. R. Nejati, M. Mohammadi, Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects, Autom. Constr., 139 (2022), 104305. https://doi.org/10.1016/j.autcon.2022.104305 doi: 10.1016/j.autcon.2022.104305
|
| [5] |
M. Juszczyk, On the search of models for early cost estimates of bridges: an SVM-based approach, Buildings, 10 (2020), 2. https://doi.org/10.3390/buildings10010002 doi: 10.3390/buildings10010002
|
| [6] |
S. Kim, C. Y. Choi, M. Shahandashti, K. R. Ryu, Improving accuracy in predicting city-level construction cost indices by combining linear ARIMA and nonlinear ANNs, J. Manage. Eng., 38 (2022), 04021093. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001008 doi: 10.1061/(ASCE)ME.1943-5479.0001008
|
| [7] |
L. Breiman, Random forests, Mach. Learn., 45 (2001), 5–32. https://doi.org/10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
|
| [8] |
C. Pierdzioch, M. Risse, Forecasting precious metal returns with multivariate random forests, Empirical Econ., 58 (2020), 1167–1184. https://doi.org/10.1007/s00181-018-1558-9 doi: 10.1007/s00181-018-1558-9
|
| [9] |
J. Yoon, Forecasting of real GDP growth using machine learning models: gradient boosting and Random forest approach, Comput. Econ., 57 (2021), 247–265. https://doi.org/10.1007/s10614-020-10054-w doi: 10.1007/s10614-020-10054-w
|
| [10] |
S. Dang, L. Peng, J. M. Zhao, J. J. Li, Z. M. Kong, A quantile regression random forest-based short-term load probabilistic forecasting method, Energies, 15 (2022), 663. https://doi.org/10.3390/en15020663 doi: 10.3390/en15020663
|
| [11] |
G. Tang, B. Pang, T. Tian, C. Zhou, Fault diagnosis of rolling bearings based on improved fast spectral correlation and optimized random forest, Appl. Sci., 8 (2018), 1859. https://doi.org/10.3390/app8101859 doi: 10.3390/app8101859
|
| [12] |
H. Latifi, B. Koch, Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands, Int. J. Remote Sens., 33 (2012), 6668–6694. https://doi.org/10.1080/01431161.2012.693969 doi: 10.1080/01431161.2012.693969
|
| [13] |
X. B. Meng, X. Z. Gao, L. Lu, Y. Liu, H. Z. Zhang, A new bio-inspired optimisation algorithm: Bird Swarm Algorithm, J. Exp. Theor. Artif. Intell., 28 (2016), 673–687. https://doi.org/10.1080/0952813X.2015.1042530 doi: 10.1080/0952813X.2015.1042530
|
| [14] |
C. Zhang, S. Yu, G. Li, Y. Xu, The recognition method of MQAM signals based on BP neural network and Bird Swarm Algorithm, IEEE Access, 9 (2021), 36078–36086. https://doi.org/10.1109/ACCESS.2021.3061585 doi: 10.1109/ACCESS.2021.3061585
|
| [15] |
Y. Yu, S. Liang, B. Samali, T. N. Nguyen, C. X. Zhai, J. C. Li, et al., Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network, Eng. Struct., 273 (2022), 115066. https://doi.org/10.1016/j.engstruct.2022.115066 doi: 10.1016/j.engstruct.2022.115066
|
| [16] |
J. H. Huan, D. H. Ma, W. Wang, X. D. Guo, Z. Y. Wang, L. C. Wu, Safety-state evaluation model based on structural entropy weight-matter element extension method for ancient timber architecture, Adv. Struct. Eng., 23 (2020), 1087–1097. https://doi.org/10.1177/1369433219886085 doi: 10.1177/1369433219886085
|
| [17] |
Y. Elfahham, Estimation and prediction of construction cost index using neural networks, time series, and regression, Alexandria Eng. J., 58 (2019), 499–506. https://doi.org/10.1016/j.aej.2019.05.002 doi: 10.1016/j.aej.2019.05.002
|
| [18] |
Y. Cao, B. Ashuri, Predicting the volatility of highway construction cost index using long short-term memory, J. Manage. Eng., 36 (2020), 1–9. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000784 doi: 10.1061/(ASCE)ME.1943-5479.0000784
|
| [19] |
S. Mao, F. Xiao, A novel method for forecasting construction cost index based on complex network, Physica A, 527 (2019), 121306. https://doi.org/10.1016/j.physa.2019.121306 doi: 10.1016/j.physa.2019.121306
|
| [20] |
E. Kaya, A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification, Mathematics, 10 (2022), 1611. https://doi.org/10.3390/math10091611 doi: 10.3390/math10091611
|
| [21] |
S. Roh, S. Tae, R. Kim, S. Park, Probabilistic analysis of major construction materials in the life cycle embodied environmental cost of Korean apartment buildings, Sustainability, 11 (2019), 846. https://doi.org/10.3390/su11030846 doi: 10.3390/su11030846
|
| [22] | Y. Liu, X. Y. Wang, H. Li, A multi-object grey target approach for group decision, J. Grgy Syst., 31 (2019), 60–72. |
| [23] |
T. Moon, D. H. Shin, Forecasting construction cost index using interrupted time-series, KSCE J. Civ. Eng., 22 (2018), 1626–1633. https://doi.org/10.1007/s12205-017-0452-x doi: 10.1007/s12205-017-0452-x
|
| [24] |
R. Slade, A. Bauen, Micro-algae cultivation for biofuels: cost, energy balance, environmental impacts and future prospects, Biomass Bioenergy, 53 (2013), 29–38. https://doi.org/10.1016/j.biombioe.2012.12.019 doi: 10.1016/j.biombioe.2012.12.019
|
| [25] |
J. Hong, G. Q. Shen, Z. Li, B. Y. Zhang, W. Q. Zhang, Barriers to promoting prefabricated construction in China: a cost-benefit analysis, J. Cleaner Prod., 172 (2018), 649–660. https://doi.org/10.1016/j.jclepro.2017.10.171 doi: 10.1016/j.jclepro.2017.10.171
|
| [26] |
L. Liu, D. Liu, H. Wu, J. W. Wang, Study on foundation pit construction cost prediction based on the stacked denoising autoencoder, Math. Probl. Eng., 2020 (2020), 8824388. https://doi.org/10.1155/2020/8824388 doi: 10.1155/2020/8824388
|
| [27] |
S. Hwang, Time series models for forecasting construction costs using time series indexes, J. Constr. Eng. Manage., 137 (2011), 656–662. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000350 doi: 10.1061/(ASCE)CO.1943-7862.0000350
|
| [28] |
S. Punia, K. Nikolopoulos, S. P. Singh, J. K. Madaan, K. Litsiou, Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail, Int. J. Prod. Res., 58 (2020), 4964–4979. https://doi.org/10.1080/00207543.2020.1735666 doi: 10.1080/00207543.2020.1735666
|
| [29] |
Z. Zou, Y. Yang, Z. Fan, H. M. Tang, M. Zou, X. L. Hu, et al., Suitability of data preprocessing methods for landslide displacement forecasting, Stochastic Environ. Res. Risk Assess., 34 (2020), 1105–1119. https://doi.org/10.1007/s00477-020-01824-x doi: 10.1007/s00477-020-01824-x
|
| [30] | L. Endlova, V. Vrbovsky, Z. Navratilova, L. Tenkl, The use of near-infrared spectroscopy in rapeseed breeding programs, Chem. Listy, 111 (2017), 524–530. Available from: https://hero.epa.gov/hero/index.cfm/reference/details/reference_id/5214159. |
| [31] |
M. A. Bujang, E. D. Omar, N. A. Baharum, A review on sample size determination for Cronbach's alpha test: a simple guide for researchers, Malays. J. Med. Sci., 25 (2018), 85–99. https://doi.org/10.21315/mjms2018.25.6.9 doi: 10.21315/mjms2018.25.6.9
|
| [32] |
Y. Yu, B. Samali, M. Rashidi, M. Mohammadi, T. N. Nguyen, G. Zhang, Vision-based concrete crack detection using a hybrid framework considering noise effect, J. Build. Eng., 61 (2022), 105246. https://doi.org/10.1016/j.jobe.2022.105246 doi: 10.1016/j.jobe.2022.105246
|
| [33] |
T. Mitsul, S. Okuyama, Measurement data selection using multiple regression analysis for precise quantitative analysis, Bunseki. Kagaku., 60 (2011), 163–170. https://doi.org/10.2116/bunsekikagaku.60.163 doi: 10.2116/bunsekikagaku.60.163
|
| [34] | M. Skitmore, D. H. Picken, The accuracy of pre-tender building price forecasts: an analysis of USA data, in Information and Communication in Construction Procurement CIB W92 Procurement System Symposium, (2000), 595–606. Available from: https://eprints.qut.edu.au/9460/. |
| [35] |
T. Jin, Y. Jiang, B. Mao, X. Wang, B. Lu, J. Qian, et al., Multi-center verification of the influence of data ratio of training sets on test results of an Al system for detecting early gastric cancer based on the YOLO-v4 algorithm, Front. Oncol., 12 (2022), 953090. https://doi.org/10.3389/fonc.2022.953090 doi: 10.3389/fonc.2022.953090
|
| [36] |
P. An, X. Li, P. Qin, Y. J. Ye, J. Y. Zhang, H. Y. Guo, et al., Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: a preliminary study, Math. Biosci. Eng., 20 (2023), 6612–6629. https://doi.org/10.3934/mbe.2023284 doi: 10.3934/mbe.2023284
|
| [37] |
C. Benard, S. Da Veiga, E. Scornet, Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA, Biometrika, 109 (2022), 881–900. https://doi.org/10.1093/biomet/asac017 doi: 10.1093/biomet/asac017
|
| [38] |
D. Karamichailidou, V. Kaloutsa, A. Alexandridis, Wind turbine power curve modeling using radial basis function neural networks and tabu search, Renewable Energy, 163 (2021), 2137–2152. https://doi.org/10.1016/j.renene.2020.10.020 doi: 10.1016/j.renene.2020.10.020
|
| [39] |
K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri, A. K. Al-Othman, Simulated annealing algorithm for photovoltaic parameters identification, Sol. Energy, 86 (2012), 266–274. https://doi.org/10.1016/j.solener.2011.09.032 doi: 10.1016/j.solener.2011.09.032
|
| [40] |
S. Gao, Y. Wang, J. Cheng, Y. Inazumi, Z. Tang, Ant colony optimization with clustering for solving the dynamic location routing problem, Appl. Math. Comput., 285 (2016), 149–173. https://doi.org/10.1016/j.amc.2016.03.035 doi: 10.1016/j.amc.2016.03.035
|
| [41] |
L. Tang, Y. Dong, J. Liu, Differential evolution with an individual-dependent mechanism, IEEE Trans. Evol. Comput., 19 (2015), 560–574. https://doi.org/10.1109/TEVC.2014.2360890 doi: 10.1109/TEVC.2014.2360890
|
| [42] |
Y. Yu, M. Rashidi, B. Samali, M. Mohammadi, T. N. Nguyen, X. X. Zhou, Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm, Struct. Health Monit., 21 (2022), 2244–2263. https://doi.org/10.1177/14759217211053546 doi: 10.1177/14759217211053546
|
| [43] |
C. Zhang, X. Wang, S. Chen, H. Li, X. X. Wu, X. Zhang, A modified random forest based on kappa measure and binary artificial bee colony algorithm, IEEE Access, 9 (2021), 117679–117690. https://doi.org/10.1109/ACCESS.2021.3105796 doi: 10.1109/ACCESS.2021.3105796
|
| [44] |
M. Reif, F. Shafait, A. Dengel, Meta-learning for evolutionary parameter optimization of classifiers, Mach. Learn., 87 (2012), 357–380. https://doi.org/10.1007/s10994-012-5286-7 doi: 10.1007/s10994-012-5286-7
|
| [45] | Y. Dong, J. Du, B. Li, Research on discrete wolf pack algorithm of mutiple choice knapsack problem, Transducer Microsyst. Technol., 34 (2015), 21–23. |
| [46] |
H. Naseri, H. Jahanbakhsh, A. Foomajd, N. Galustanian, M. M. Karimi, E. O. D. Waygood, A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying Whale Optimization Algorithm and random forest regression, Int. J. Pavement Eng., 2022 (2022). https://doi.org/10.1080/10298436.2022.2147672 doi: 10.1080/10298436.2022.2147672
|
| [47] |
D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artif. Intell. Rev., 42 (2014), 21–57. https://doi.org/10.1007/s10462-012-9328-0 doi: 10.1007/s10462-012-9328-0
|
| [48] |
Y. Yu, J. Li, J. Li, Y. Xia, Z. H. Ding, B. Samali, Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion, Dev. Built Environ., 14 (2023), 100128. https://doi.org/10.1016/j.dibe.2023.100128 doi: 10.1016/j.dibe.2023.100128
|
| [49] |
G. Huang, G. B. Huang, S. Song, K. Y. You, Trends in extreme learning machines: a review, Neural Networks, 61 (2015), 32–48. https://doi.org/10.1016/j.neunet.2014.10.001 doi: 10.1016/j.neunet.2014.10.001
|
| [50] | M. Kayri, I. Kayri, M. T. Gencoglu, The performance comparison of multiple linear regression, random forest and artificial neural network by using photovoltaic and atmospheric data, in 2017 14th International Conference on Engineering of Modern Electric Systems (EMES), (2017), 1–4. https://doi.org/10.1109/EMES.2017.7980368 |
| [51] |
Y. Wang, A. W. Kandeal, A. Swidan, S. W. Sharshir, G. B. Abdelaziz, M. A. Halim, et al., Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm, Appl. Therm. Eng., 184 (2021), 116233. https://doi.org/10.1016/j.applthermaleng.2020.116233 doi: 10.1016/j.applthermaleng.2020.116233
|
| [52] |
A. B. Owen, Better estimation of small sobol' sensitivity pndices, ACM Trans. Model. Comput. Simul., 23 (2013), 1–17. https://doi.org/10.1145/2457459.2457460 doi: 10.1145/2457459.2457460
|
| [53] |
S. Kucherenko, O. V. Klymenko, N. Shah, Sobol' indices for problems defined in non-rectangular domains, Reliab. Eng. Syst. Saf., 167 (2017), 218–231. https://doi.org/10.1016/j.ress.2017.06.001 doi: 10.1016/j.ress.2017.06.001
|
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Zhishan Zheng, Lin Zhou, Han Wu, Lihong Zhou. Construction cost prediction system based on Random Forest optimized by the Bird Swarm Algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15044-15074. doi: 10.3934/mbe.2023674
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