臺灣博碩士論文加值系統

本研究提出數種機器學習與深度學習模型，用於預測軟黏土地盤中支撐開挖所引起的牆體變形及其位置；同時，探討應用於三層軟黏土地盤支撐開挖施工的3D Plaxis反分析方法，其中建築物採用自下而上的順打工法。我們選擇硬化土（HS）模型的有限元素方法（FEM）來檢測軟黏土地盤的行為。軟黏土地盤的HS模型參數是根據標準貫入試驗（SPT）與深度之間的相關性進行選擇。基於五個輸入參數，分別命名為深基礎最後開挖階段的開挖深度（He）、測點深度（Hi）、鑽孔樁軸向剛度（EA）與支柱架EA總剛度之比，支柱框架2乘以鑽孔樁擋土牆的長邊（Bj，j=1..5）與周長（P）之比稱為Fj（j=1..5），軸向剛度（ 鑽孔樁的 EA) 和水泥深層攪拌 (CDM) 的 EA 乘以鑽孔樁擋土牆的長邊和周長之比 Kj (j= 1..5)，以及根據樁擋牆的起點和終點計算的樁擋牆位置測斜儀的最小距離除以測點深度（Lmin/Hi）；同時，利用施工時記錄的最後一個開挖階段（或開挖的最後一天）的264個側牆撓度案例來估計樁擋牆的撓度及其位置。在完成的預測分析中，預測的均方根誤差（RMSE）、平均絕對誤差（MAE）和平均絕對百分比誤差（MAPE）值分別低於16.73mm、12.31mm和31%。本研究之貢獻包括運用機器學習與3D Plaxis結合，評估開挖施工過程中的可行性和效率；並應用機器學習結合監測數據，監測施工過程中開挖土坑的位移。此結合旨在幫助施工安全進行，及時解決問題，節省施工成本，縮短施工時間。
關鍵詞：開挖、預測、機器學習、數值模擬、側壁撓度。

This research presents several machine learning and deep learning models to predict wall deflections and their positions by braced excavation in soft clay soil; examined inverse analysis method with 3D Plaxis for the construction based on braced excavation of three soft clay soil layers, in which the building was constructed by applying the method of bottom-up. The finite element method (FEM) with the hardening soil (HS) model was chosen to check the behaviour of soft clay soil. The parameters of the HS model for the soil layers were selected from the correlation between standard penetration test (SPT) and depth. Based on five input parameters were named the excavated depth at the last excavated stage of the deep excavation (He), depth of measuring points (Hi), the rate between the axial stiffness (EA) of bored pile and EA total of strut frame1, strut frame2 multiply ratio of length side (Bj, ????= 1,5̅̅̅̅) and perimeter (P) of the bored pile retaining wall called Fj (????= 1,5̅̅̅̅), the rate between the axial stiffness (EA) of bored pile and EA of cement deep mixing (CDM) multiply ratio of length side and perimeter of the bored pile retaining wall called Kj (????= 1,5̅̅̅̅), and the minimum distance of inclinometer in pile retaining wall location calculated from begin and end points of pile retaining wall divide depth of excavation (Lmin/Hi); at the same time, the 264 cases of lateral wall deflection at the last excavated phase (or the last day of excavation) recorded at construction were deployed to estimate bored pile retaining wall deflection and their positions. In the completed predicting analysis, the scores of accuracies were accepted for predicting with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values were lower than 16.73mm, 12.31mm, and 31%, respectively. The outstanding contributions of this research include using machine learning in combination with 3D Plaxis to evaluate the feasibility and efficiency of excavation construction; using machine learning in combination with monitoring data to monitor the displacement of the excavation pit during construction. This combination aims to help the construction work safely, solve the pitfalls in time, save construction costs, and shorten the construction time.
Keywords: Excavation, Prediction, Machine learning, Numerical simulation, Wall deflection.

LIST OF CONTENTS
Abstract ............................................................................................. ii
ACKNOWLEDGE .......................................................................................... iii
LIST OF CONTENTS .................................................................................... iv
LIST OF FIGURES ....................................................................................... vii
LIST OF ABBREVIATIONS .......................................................................... vi
Chapter 1. INTRODUCTION .......................................................................... 1
1.1. Research background .......................................................................... 1
1.2. Research purpose................................................................................. 5
1.3. Scope of research ................................................................................. 6
1.4. Research limitation ............................................................................... 7
1.5. Research structure ............................................................................... 8
1.6. Dissertation outline .............................................................................. 9
Chapter 2. Literature review ...................................................................... 11
2.1. Lateral wall deflection due to deep excavation ............................... 11
2.2. Influence factors on deep excavation ............................................... 13
2.3. Long Short-Term Memory model ...................................................... 16
2.4. Bidirectional Long Short-Term Memory model .............................. 17
2.5. Feed-Forward Neural Network Back-Propagation model .............. 18
2.6. Support Vector Regression model .................................................... 19
Chapter 3. STUDY AREA AND DATA COLLECTION ............................... 21
3.1. An overview of Ho Chi Minh City’s geology ..................................... 21
3.2. The project site’s description ............................................................ 24
3.3. Data obtained at the project site ....................................................... 28
3.3.1. Soil property ...................................................................................... 28
3.3.2. CDM property .................................................................................... 29
3.3.3. Strut frame property ......................................................................... 29
3.3.5. Pile wall property .............................................................................. 31
3.3.6. Process of measuring bored pile wall deflection by inclinometer sensor ........... 32
Chapter 4. METHODOLOGY OF NUMERICAL SIMULATION AND MACHINE LEARNING ........... 35
4.1. Numerical simulation using 3D Plaxis .............................................. 35
4.1.1. Input data ......................................................................................... 35
4.1.2. Model development ........................................................................ 35
4.1.3. Output data ...................................................................................... 37
4.2. Machine learning................................................................................. 37
4.2.1. Input data ......................................................................................... 37
4.2.2. Model development ........................................................................ 40
4.2.3. Output data ...................................................................................... 46
Chapter 5. RESULTS AND DISCUSSIONS ................................................ 47
5.1. Simulation of the deep excavated pit using 3D Plaxis ................... 47
5.2. Using machine learning models to predict wall deformation......... 56
5.3. Comparison between 3D Plaxis simulation and machine learning models .... 61
Chapter 6. CONCLUSIONS AND RECOMMENDATIONS .................................. 65
6.1. Conclusions ............................................................................. 65
6.2. Recommendations ...................................................................... 68

REFERENCE ............................................................................. 71
APPENDIX ..............................................................................84

REFERENCE
[1]
Kung, G. T., Hsiao, E. C., Schuster, M., & Juang, C. H. (2007). A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Computers and Geotechnics, 34(5), 385-396.
[2]
Ou, C. Y. (2014). Deep excavation: Theory and practice. Crc Press, London, England.
[3]
Moh, Z. C., & Hwang, R. N. (2005). Geotechnical considerations in the design and construction of subways in urban areas. In Seminar on Recent Developments on Mitigation of Natural Disasters, Urban Transportation and Construction Industry (Vol. 30).
[4]
Zhao, H., Liu, W., Guan, H., & Fu, C. (2021). Analysis of diaphragm wall deflection induced by excavation based on machine learning. Mathematical Problems in Engineering, 2021, 1-10.
[5]
Liu, W., Shi, P., Li, H., & Wang, F. (2019). Observed performance of three adjacent 48 m depth concrete diaphragm wall panels in silty soils. Canadian Geotechnical Journal, 56(11), 1736-1742.
[6]
Zhu, G. (2021). Mindlin solution on ground deformation caused by the trench excavation during installation of concrete diaphragm wall panels. Scientific reports, 11(1), 19199.
[7]
Zhao, H. J., Liu, W., Shi, P. X., Du, J. T., & Chen, X. M. (2021). Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation. Acta Geotechnica, 16(11), 3631-3645.
[8]
Chen, D. P., Qian, C. X., & Liu, C. L. (2010). A numerical simulation approach to calculating hygrothermal deformation of concrete based on heat and moisture transfer in porous medium. International Journal of Civil Engineering, 8(4), 287-296.
[9]
Zhang, Q. Q., Liu, S. W., Feng, R. F., & Li, X. M. (2019). Analytical method for prediction of progressive deformation mechanism of existing piles due to excavation beneath a pile-supported building. International Journal of Civil Engineering, 17, 751-763.
[10]
Mahinroosta, R., & Alizadeh, A. (2012). Simulation of collapse settlement in rockfill material due to saturation. International Journal of Civil Engineering, 10(2), 93-99.
[11]
Yong, C. C., & Oh, E. (2016). Modelling ground response for deep excavation in soft ground. GEOMATE Journal, 11(26), 2633-2642.
[12]
Aljanabi, K. R., & AL-Azzawi, O. M. (2021). Neural network application in forecasting maximum wall deflection in homogenous clay. International Journal of Geo-Engineering, 12, 1-18.
[13]
Hsiung, B. C. B., & Phan, H. K. (2023). Exploration of maximum wall deflection and stability for deep excavation in loose to medium-dense sand. Acta Geotechnica, 1-17.
[14]
Long, M. (2002). Closure: Database for retaining wall and ground movements due to deep excavations. J. Geotech. Geoenviron. Engrg., ASCE.
[15]
Xiao, H., Zhou, S., & Sun, Y. (2019). Wall deflection and ground surface settlement due to excavation width and foundation pit classification. KSCE Journal of Civil Engineering, 23, 1537-1547.
[16]
Mei, Y., Zhou, D., Wang, X., Zhao, L., Shen, J., Zhang, S., & Liu, Y. (2021). Deformation law of the diaphragm wall during deep foundation pit construction on lake and sea soft soil in the Yangtze River Delta. Advances in Civil Engineering, 2021, 1-11.
[17]
Goh, A. T. C., Zhang, F., Zhang, W., Zhang, Y., & Liu, H. (2017). A simple estimation model for 3D braced excavation wall deflection. Computers and Geotechnics, 83, 106-113.
[18]
Ou, C. Y., & Hsieh, P. G. (2011). A simplified method for predicting ground settlement profiles induced by excavation in soft clay. Computers and Geotechnics, 38(8), 987-997..
[19]
Hsiung, B. C. B., Yang, K. H., Aila, W., & Hung, C. (2016). Three-dimensional effects of a deep excavation on wall deflections in loose to medium dense sands. Computers and Geotechnics, 80, 138-151.
[20]
Hsiung, B. C. B., Yang, K. H., Aila, W., & Ge, L. (2018). Evaluation of the wall deflections of a deep excavation in Central Jakarta using three-dimensional modeling. Tunnelling and Underground Space Technology, 72, 84-96.
[21]
Liu, W., Shi, P., Cai, G., & Gan, P. (2022). A three-dimensional mechanism for global stability of slurry trench in frictional soils. European Journal of Environmental and Civil Engineering, 26(2), 594-619.
[22]
Mohamed, A. A. E. Z. (2017). Effect of diaphragm wall construction on adjacent deep foundation, Freiberg, Germany.
[23]
Liu, W., Shi, P., Cai, G., & Cao, C. (2021). Seepage on local stability of slurry trench in deep excavation of diaphragm wall construction. Computers and Geotechnics, 129, 103878.
[24]
He, X., Xu, H., Sabetamal, H., & Sheng, D. (2020). Machine learning aided stochastic reliability analysis of spatially variable slopes. Computers and Geotechnics, 126, 103711.
[25]
Pourtaghi, A., & Lotfollahi-Yaghin, M. A. (2012). Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunnelling and Underground Space Technology, 28, 257-271.
[26]
Zhang, W., Zhang, R., Wang, W., Zhang, F., & Goh, A. T. C. (2019). A multivariate adaptive regression splines model for determining horizontal wall deflection envelope for braced excavations in clays. Tunnelling and Underground Space Technology, 84, 461-471.
[27]
Qu, X., Yang, J., & Chang, M. (2019). A deep learning model for concrete dam deformation prediction based on RS-LSTM. Journal of Sensors, 2019, 1-14.
[28]
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), 818.
[29]
Zhang, W. G., Goh, A. T. C., Goh, K. H., Chew, O. Y. S., Zhou, D., & Zhang, R. (2018). Performance of braced excavation in residual soil with groundwater drawdown. Underground Space, 3(2), 150-165.
[30]
Abba, S. I., Pham, Q. B., Usman, A. G., Linh, N. T. T., Aliyu, D. S., Nguyen, Q., & Bach, Q. V. (2020). Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant. Journal of Water Process Engineering, 33, 101081.
[31]
vom Scheidt, F., Medinová, H., Ludwig, N., Richter, B., Staudt, P., & Weinhardt, C. (2020). Data analytics in the electricity sector–A quantitative and qualitative literature review. Energy and AI, 1, 100009.
[32]
Gupta, D., Hazarika, B. B., Berlin, M., Sharma, U. M., & Mishra, K. (2021). Artificial intelligence for suspended sediment load prediction: a review. Environmental Earth Sciences, 80(9), 346.
[33]
Zhang, B., Zhang, H., Zhao, G., & Lian, J. (2020). Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environmental Modelling & Software, 124, 104600.
[34]
Rao, K. S., Devi, G. L., & Ramesh, N. (2019). Air quality prediction in Visakhapatnam with LSTM based recurrent neural networks. Int. J. Intell. Syst. Appl, 11(2), 18-24.
[35]
Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors, 17(2), 273.
[36]
Hung, N. K., & Phienwej, N. (2016). Practice and experience in deep excavations in soft soil of Ho Chi Minh City, Vietnam. KSCE Journal of Civil Engineering, 20, 2221-2234.
[37]
Duc, T. N. (2022). Soft Clay Stiffness Measured in Ho Chi Minh City with Oedometer Tests for Deep Excavation Calculation. In CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure:Proceedings of the 6th International Conference on Geotechnics, Civil Engineering and Structures (pp. 1181-1189). Springer Singapore.
[38]
Nguyen, B. P., Ngo, C. P., Tran, T. D., Bui, X. C., & Doan, N. P. (2022). Finite element analysis of deformation behavior of deep excavation retained by diagram wall in Ho Chi Minh city. Indian Geotechnical Journal, 52(4), 989-999.
[39]
Lai, V. Q., Le, M. N., Huynh, Q. T., & Do, T. H. (2020). Performance analysis of a combination between D-wall and Secant pile wall in upgrading the depth of basement by Plaxis 2D: a case study in Ho Chi Minh city. In ICSCEA 2019: Proceedings of the International Conference on Sustainable Civil Engineering and Architecture (pp. 745-755). Springer Singapore.
[40]
Clough, G. W. (1990). Construction Induced Movements of Insitu Wall, Design and Performance of Earth Retaining Structure. In ASCE (pp. 439-479).
[41]
Ou, C. Y., Hsieh, P. G., & Chiou, D. C. (1993). Characteristics of ground surface settlement during excavation. Canadian geotechnical journal, 30(5), 758-767.
[42]
Ng, C. W. (1999). Stress paths in relation to deep excavations. Journal of geotechnical and geoenvironmental engineering, 125(5), 357-363.
[43]
Ng, C. W. W., Hong, Y., Liu, G. B., & Liu, T. (2012). Ground deformations and soil–structure interaction of a multi-propped excavation in Shanghai soft clays. Géotechnique, 62(10), 907-921.
[44]
Tan Y, Wang D (2013) Characteristics of a large-scale deep foundation pit excavated by the central-island technique in Shanghai soft clay. II:Top-down construction of the peripheral rectangular pit. Journal of Geotechnical and Geoenvironmental Engineering 139(11):1894–1910.
[45]
Tan Y, Wang D (2013) Characteristics of a large-scale deep foundation pit excavated by the central-island technique in Shanghai soft clay. I: Bottom-up construction of the central cylindrical shaft. Journal of Geotechnical and Geoenvironmental Engineering 139(11):1875–1893.
[46]
Liu, H., Li, P., & Liu, J. (2011). Numerical investigation of underlying tunnel heave during a new tunnel construction. Tunnelling and Underground Space Technology, 26(2), 276-283.
[47]
Yang, D., Gu, C., Zhu, Y., Dai, B., Zhang, K., Zhang, Z., & Li, B. (2020). A concrete dam deformation prediction method based on LSTM with attention mechanism. IEEE Access, 8, 185177-185186.
[48]
Choi, Y., Lee, J., & Kong, J. (2020). Performance degradation model for concrete deck of bridge using pseudo-LSTM. Sustainability, 12(9), 3848.
[49]
Ranjbar, I., & Toufigh, V. (2022). Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete. Cement and Concrete Research, 162, 107003.
[50]
Hu, Y., Gu, C., Meng, Z., Shao, C., & Min, Z. (2022). Prediction for the settlement of concrete face rockfill dams using optimized LSTM model via correlated monitoring data. Water, 14(14), 2157.
[51]
Li, J., Li, P., Guo, D., Li, X., & Chen, Z. (2021). Advanced prediction of tunnel boring machine performance based on big data. Geoscience Frontiers, 12(1), 331-338.
[52]
Liu, M., Liao, S., Yang, Y., Men, Y., He, J., & Huang, Y. (2021). Tunnel boring machine vibration-based deep learning for the ground identification of working faces. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1340-1357.
[53]
Shen, S. L., Atangana Njock, P. G., Zhou, A., & Lyu, H. M. (2021). Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning. Acta Geotechnica, 16(1), 303-315.
[54]
Chen, W., Luo, Q., Liu, J., Wang, T., & Wang, L. (2022). Modeling of frozen soil-structure interface shear behavior by supervised deep learning. Cold Regions Science and Technology, 200, 103589.
[55]
Huang, Z., Zhang, D., & Zhang, D. (2021). Application of ANN in predicting the cantilever wall deflection in undrained clay. Applied Sciences, 11(20), 9760.
[56]
Zhang, R., Wu, C., Goh, A. T., Böhlke, T., & Zhang, W. (2021). Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning. Geoscience Frontiers, 12(1), 365-373.
[57]
Chen, R., Zhang, P., Wu, H., Wang, Z., & Zhong, Z. (2019). Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 13, 1363-1378.
[58]
Goh, A. T., Wong, K. S., & Broms, B. B. (1995). Estimation of lateral wall movements in braced excavations using neural networks. Canadian Geotechnical Journal, 32(6), 1059-1064.
[59]
Haruna, S. I., Zhu, H., Umar, I. K., Shao, J., Adamu, M., & Ibrahim, Y. E. (2022, May). Gaussian process regression model for the prediction of the compressive strength of polyurethane-based polymer concrete for runway repair: A comparative approach. In IOP Conference Series: Earth and Environmental Science, Vol. 1026, No. 1, p. 012007. IOP Publishing.
[60]
Ahmad, M. S., Adnan, S. M., Zaidi, S., & Bhargava, P. (2020). A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Construction and building materials, 248, 118475.
[61]
Keshtegar, B., Nehdi, M. L., Trung, N. T., & Kolahchi, R. (2021). Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing, 112, 107739.
[62]
Bai, C., Nguyen, H., Asteris, P. G., Nguyen-Thoi, T., & Zhou, J. (2020). A refreshing view of soft computing models for predicting the deflection of reinforced concrete beams. Applied Soft Computing, 97, 106831.
[63]
Shaqadan, A. (2020). Prediction of concrete strength using support vector machines algorithm. In Materials Science Forum, Vol. 986, pp. 9-17. Trans Tech Publications Ltd.
[64]
San Ha, P. T. (2007). Applying kriging to predict the distribution of the holocene soft stratum in the urban area of HoChiMinh City. Journal of Science and Technology Development, 10(2), 43-54.
[65]
Grădinaru, S. R., Fan, P., Iojă, C. I., Niță, M. R., Suditu, B., & Hersperger, A. M. (2020). Impact of national policies on patterns of built-up development: an assessment over three decades. Land Use Policy, 94, 104510.
[66]
Huynh, Q. T., Lai, V. Q., Tran, V. T., & Nguyen, M. T. (2020). Back analysis on deep excavation in the thick sand layer by hardening soil small model. In ICSCEA 2019: Proceedings of the International Conference on Sustainable Civil Engineering and Architecture (pp. 659-668). Springer Singapore.
[67]
Chung, K. L. T., & San Ha, P. T. (2017). Evaluation of SHANSEP parameters for soft clay in HCM City. VNUHCM Journal of Science and Technology Development, 20(K9), 31-37.
[68]
Luan, V. N., & Nu, N. T. (2021). Consolidation Properties of Ho Chi Minh City Soil, Vietnam. The Iraqi Geological Journal, 1-10.
[69]
Sang-To, T., Hoang-Le, M., Vu-Tran, M., Wahab, M. A., & Cuong-Le, T. (2022). Estimation Displacement of Diaphragm Wall Using Hardening Soil Versus Mohr–Coulomb Model. In Recent Advances in Structural Health Monitoring and Engineering Structures: Select Proceedings of SHM and ES 2022, pp. 345-351. Singapore: Springer Nature Singapore.
[70]
Ngo Duc, T., Vo, P., & Tran Thi, T. (2020, July). Determination of Unloading—Reloading Modulus and Exponent Parameters (m) for Hardening Soil Model of Soft Soil in Ho Chi Minh City. In ICSCEA 2019: Proceedings of the International Conference on Sustainable Civil Engineering and Architecture, pp. 677-689. Singapore: Springer Singapore.
[71]
Nguyen, V. H., & Nikiforova, N. (2018). The choice of soil models in the design of deep excavation in soft soils of Viet Nam. In MATEC Web of Conferences, Vol. 251, p. 04033. EDP Sciences.
[72]
Lam, S. Y., Haigh, S. K., & Bolton, M. D. (2014). Understanding ground deformation mechanisms for multi-propped excavation in soft clay. Soils and Foundations, 54(3), 296-312.
[73]
Hashash, Y. M., Marulanda, C., Ghaboussi, J., & Jung, S. (2006). Novel approach to integration of numerical modeling and field observations for deep excavations. Journal of Geotechnical and Geoenvironmental Engineering, 132(8), 1019-1031.
[74]
Jen, L. C. (1997). The design and performance of deep excavations in clay (Doctoral dissertation, Massachusetts Institute of Technology).
[75]
Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traffic forecast. IET Intelligent Transport Systems, 11(2), 68-75.
[76]
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
[77]
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
[78]
Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991.
[79]
Khoshhal, J., & Mokarram, M. (2012). Model for prediction of evapotranspiration using MLP neural network. International Journal of Environmental Sciences, 3(3), 1000-1009.
[80]
Ramchoun, H., Ghanou, Y., Ettaouil, M., & Janati Idrissi, M. A. (2016). Multilayer perceptron: Architecture optimization and training.
[81]
Ashiquzzaman, A., & Tushar, A. K. (2017, February). Handwritten Arabic numeral recognition using deep learning neural networks. In 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1-4. IEEE.
[82]
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14, 199-222.
[83]
Zhang, H., Cheng, X., Li, Y., & Du, X. (2022). Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning. Journal of Building Engineering, 50, 104145.
[84]
Chen, P., Niu, A., Liu, D., Jiang, W., & Ma, B. (2018, August). Time series forecasting of temperatures using SARIMA: An example from Nanjing. In IOP Conference Series: Materials Science and Engineering, Vol. 394, p. 052024. IOP Publishing.
[85]
Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H., & Rehman, M. U. (2019). A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access, 7, 28309-28318.
[86]
Xia, W., Zhu, W., Liao, B., Chen, M., Cai, L., & Huang, L. (2018). Novel architecture for long short-term memory used in question classification. Neurocomputing, 299, 20-31.
[87]
Kardani, N., Zhou, A., Nazem, M., & Shen, S. L. (2020). Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotechnical and Geological Engineering, 38, 2271-2291.
[88]
Touzani, S., Granderson, J., & Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158, 1533-1543.