References
- Géron, A. (2020). Handson machine learning with Scikit-learn, Keras & tensorflow (3rd ed.) Helion.
- Chrystal, R. China, ‘Five machine learning types to know’, IBM Think. [Online]. https://www.ibm.com/think/topics/machine-learning-types.
- Lowe, D.J., Emsley, M.W., Harding, A. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750–758. doi: 10.1061/(ASCE)0733-9364(2006)132:7(750).
- Thomas, N., Thomas, A.V. (Nov. 2016). Regression modelling for prediction of construction cost and duration. AMM, 857, 195–199. doi: 10.4028/www.scientific.net/AMM.857.195.
- Sheshadri, A., Marathe, S., Rodrigues, A.P., Nieświec, M. (Mar. 2025). Predictive modelling of pavement quality fibre-reinforced alkali-activated nano-concrete mixes through artificial intelligence. Studia Geotechnica et Mechanica, 46(s1), 389–416. doi: 10.2478/sgem-2025-0007.
- Fang, Q., Ibarra-Castanedo, C., Garrido, I., Duan, Y., Maldague, X. (May 2023). Automatic detection and identification of defects by deep learning algorithms from pulsed thermography data. Sensors, 23(9), 4444. doi: 10.3390/s23094444.
- Ji, H., Hwang, I., Kim, J., Lee, S., Lee, W. (Dec. 2024). Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment. PLoS ONE, 19(12), e0314931. doi: 10.1371/journal.pone.0314931.
- Liebert, A., Weber, W., Reif, S., Zimmering, B., Niggemann, O. (2022). Anomaly detection with autoencoders as a tool for detecting sensor malfunctions. In 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 1–8). doi: 10.1109/ICPS51978.2022.9816908.
- Ghanbari, M., Hafizi, K., Bano, M., Ebrahimi, A., Hosseinzadeh, N. (2023). GPR data processing framework for detecting subsurface utilities. doi: 10.13140/RG.2.2.13263.15528.
- Li, Y., Dang, P., Xu, X., Lei, J. (Mar. 2023). Deep learning for improved subsurface imaging: Enhancing GPR clutter removal performance using contextual feature fusion and enhanced spatial attention. Remote Sensing, 15(7), 1729. doi: 10.3390/rs15071729.
- Stergiou, K., Ntakolia, C., Varytis, P., Koumoulos, E., Karlsson, P., Moustakidis, S. (Mar. 2023). Enhancing property prediction and process optimization in building materials through machine learning: A review. Computational Materials Science, 220, 112031. doi: 10.1016/j.commatsci.2023.112031.
- Jocz, M., Lefik, M. (Dec. 2023). Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network. Studia Geotechnica et Mechanica, 45(s1), 405–415. doi: 10.2478/sgem-2023-0023.
- Rolf, B., Jackson, I., Müller, M., Lang, S., Reggelin, T., Ivanov, D. (Oct. 2023). A review on reinforcement learning algorithms and applications in supply chain management. International Journal of Production Research, 61(20), 7151–7179. doi: 10.1080/00207543.2022.2140221.
- Farrokhi, F., Rahimi, S. (Sep. 2020). Supervised probabilistic failure prediction of tuned mass damper-equipped high steel frames using machine learning methods. Studia Geotechnica et Mechanica, 42(3), 179–190. doi: 10.2478/sgem-2019-0043.
- Rys, D. (2015). Heavy vehicle road loads and their impact on the fatigue durability of flexible and semi-rigid pavement structures. Politechnika Gdańska Wydział Inżynierii Lądowej i Środowiska, Gdańsk (p. 201). https://pbc.gda.pl/Content/50418/phd_ry%C5%9B_dawid.pdf.
- Ryś, D., Judycki, J., Jaskuła, P. (2014). The impact of overloaded vehicles on the durability of asphalt pavements. Logistyka, 6, 9318–9328.
- Normann, O.K., Hopkins, R.C. (1952). Weighing vehicles in motion. Highway Research Board, 50(221), 29.
- Burnos, P. (2014). Weighing road vehicles in motion. Part 1: The impact of overloaded vehicles on pavement. Drogownictwo, 6/2014.
- Burnos, P. (2014). Weighing Road Vehicles in Motion. Part 2: Types and Characteristics of Weigh-In-Motion (WIM) systems. Drogownictwo, 6/2014.
- Burnos, P. (2014). Weighing road vehicles in motion. Part 3: Pressure sensors used in Weigh-In-Motion (WIM) systems. Drogownictwo, 6/2014.
- Burnos, P. (2014). Weighing road vehicles in motion. Part 4: Accuracy assessment of Weigh-In-Motion (WIM) systems. Drogownictwo, 6/2014.
- Moses, F. (Jan. 1979). Weigh-In-Motion system using instrumented bridges. Transportation Engineering Journal of ASCE, 105(3), 233–249. doi: 10.1061/TPEJAN.0000783.
- Cardini, A.J., DeWolf, J.T. (Nov. 2009). Implementation of a long-term bridge Weigh-In-Motion system for a steel girder bridge in the interstate highway system. Journal of Bridge Engineering, 14(6), 418–423. doi: 10.1061/(ASCE)1084-0702(2009)14:6(418).
- Quilligan, M. (2003). Bridge weigh-in motion: Development of a 2-D multi-vehicle algorithm. Trita- BKN. Bulletin, 69, 144.
- O’Brien, E., Žnidarič, A., Eds. (2001). Weighing-in-motion of axles and vehicles for Europe (WAVE). Report of work package 1.2: Bridge WIM systems (B-WIM). Ljubljana: Zavod za gradbeništvo Slovenije.
- Kalin, J., Žnidari, A. (2019). Practical implementation of Nothing-On-the-Road Bridge Weigh-In-Motion system. https://hvttforum.org/wp- content/uploads/2019/11/Practical-Implementation-of-Nothing-on-the-Road-Bridge-Weigh-In-Motion-System-Kalin.pdf.
- Peters, R. (Aug. 1984). AXWAY - a system to obtain vehicle axle weights. Presented at the 12th ARRB Conference, Hobart, Hobart, TAS, Australia. Vermont South, VIC, Australia: ARRB Group Limited.
- Jacob, B., Brien, E.J.O., Jehaes, S. (2002). Weigh-in-motion of road vehicles. Final report of the COST 323 Action (WIM-LOAD). Laboratoire Central des Ponts et Chausse ́es, Paris. https://www.is-wim.org/doc/wim_eu_specs_cost323.pdf.
- Li, C.Y., Wang, C., Yang, Q.X., Qi, T.Y. (Sep. 2022). Identification of vehicle loads on an orthotropic deck steel box beam bridge based on optimal combined strain influence lines. Applied Sciences, 12(19), 9848. doi: 10.3390/app12199848.
- Chatterjee, P., O’Brien, E.J., Li, Y., González, A. (Nov. 2006). Wavelet domain analysis for identification of vehicle axles from bridge measurements. Computers & Structures, 84(28), 1792–1801. doi: 10.1016/j.compstruc.2006.04.013.
- Yu, Y., Cai, C.S., Cai, C.S., Cai, C.S., Deng, L. (Oct. 2017). Vehicle axle identification using wavelet analysis of bridge global responses. Journal of Vibration and Control, 23(17), 1077546315623147. doi: 10.1177/1077546315623147.
- Dunne, D., O’Brien, E.J., Basu, B., Gonzalez, A. (Jan. 2005). Bridge WIM systems with Nothing On the Road (NOR). In Proceedings of the 4th International WIM Conference (pp. 109–117). Zurich: International Society for Weigh-in-Motion.
- Machelski, C., Hildebrand, M. (Feb. 2015). Efficiency of monitoring system of a cable-stayed bridge for investigation of live loads and pier settlements. Journal of Civil Structural Health Monitoring, 5(1), 1–9. doi: 10.1007/s13349-014-0074-7.
- Chan, T.H.T., Law, S.S., Yung, T.H. (Oct. 2000). Moving force identification using an existing prestressed concrete bridge. Engineering Structures, 22(10), 1261–1270. doi: 10.1016/s0141-0296(99)00084-x.
- Zhu, X., Law, S.S., Law, S.S. (Sep. 2000). Identification of vehicle axle loads from bridge dynamic responses. Journal of Sound and Vibration, 236(4), 705–724. doi: 10.1006/jsvi.2000.3021.
- Asnachinda, P., Pinkaew, T., Laman, J.A. (Oct. 2008). Multiple vehicle axle load identification from continuous bridge bending moment response. Engineering Structures, 30(10), 2800–2817. doi: 10.1016/j.engstruct.2008.02.018.
- Dowling, J., O’Brien, E.J., González, A. (Nov. 2012). Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem. Engineering Structures, 44(44), 13–22. doi: 10.1016/j.engstruct.2012.05.047.
- Law, S., Bu, J., Zhu, X.Q. (2004). Vehicle axle loads identification using finite element method. Engineering Structures, 26(8), 1143–1153. doi: 10.1016/j.engstruct.2004.03.017.
- Viola, P., Jones, M. (Dec. 2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (p. I–I). doi: 10.1109/CVPR.2001.990517.
- Dalal, N., Triggs, B. (Jun. 2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (Vol. 1, pp. 886–893). doi: 10.1109/CVPR.2005.177.
- Girshick, R.B., Donahue, J., Darrell, T., Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587).
- Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y. et al. (Oct. 2016). SSD: Single shot multibox detector. Presented at the Computer Vision – ECCV 2016, in Lecture Notes in Computer Science (Vol. 9905). Springer. doi: 10.1007/978-3-319-46448-0_2.
- Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016). You only look once: Unified, real-time object detection (p. 788). doi: 10.1109/CVPR.2016.91.
- Dai, J., Li, Y., He, K., Sun, J. (2023). R-FCN: Object detection via region-based fully convolutional networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems, in NIPS’16 (p. 9). Barcelona, Spain: Curran Associates Inc. https://arxiv.org/abs/1605.06409.
- Ekberg, P., Daemi, B., Mattsson, L. (Feb. 2017). 3D precision measurements of meter-sized surfaces using low cost illumination and camera techniques. Measurement Science and Technology, 28, 045403. doi: 10.1088/1361-6501/aa5ae6.
- Illingworth, J., Kittler, J. (1987). The adaptive Hough transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5), 690–698. doi: 10.1109/TPAMI.1987.4767964.
- Winkler, S., Yu, H., Zhou, Z. (Feb. 2007). Tangible mixed reality desktop for digital media management. In A.J. Woods, N.A. Dodgson, J.O. Merritt, M.T. Bolas, I.E. McDowall (Eds.), Presented at the Electronic Imaging 2007 (p. 64901S). San Jose, CA, USA. doi: 10.1117/12.703906.
- Hartley, R.I., Zisserman, A. (2004). Multiple view geometry in computer vision, second. Cambridge University Press, ISBN: 0521540518.
- Chen, S., Guo, W. (2023). Auto-encoders in deep learning—a review with new perspectives. Mathematics, 11(8), 1777. doi: 10.3390/math11081777.
- LeCun, Y., Cortes, C. (2005). The MNIST database of handwritten digits. https://api.semanticscholar.org/CorpusID:60282629.
- Hebb, D.O. (1949). The organization of behavior. Wiley.
- Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K. (Aug. 2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9(4), 611–629. doi: 10.1007/s13244-018-0639-9.
- Makone, A. (2020). The landscape of deep learning based object detection techniques in computer vision. doi: 10.13140/RG.2.2.12679.42401.
- Kingma, D.P., Ba, J. (Jan. 29, 2017). Adam: A method for stochastic optimization. arXiv: arXiv:1412.6980. Accessed: Feb. 27, 2024. http://arxiv.org/abs/1412.6980.