Assessment and Application of Deep Learning Algorithms in Civil Engineering

Yıl 2020, Cilt: 7 Sayı: 2, 906 - 922, 31.05.2020

Melda Alkan Çakıroğlu , Ahmet Ali Süzen

https://doi.org/10.31202/ecjse.679113

Cited By: 1

Öz

In this study, the applicability of deep learning algorithms in the field of civil engineering has been investigated. Firstly, the information that is about deep learning algorithms has been given. Additionally, deep learning applications, which are made, in subjects such as classification, estimation, and interpretation in the field of civil engineering, have been examined. The applications are elaborated according to civil engineering's sub-branches that transportation, geotechnical and construction. The contributions of the realized applications' in view of success rates to civil engineering that were analyzed. As a result of the study, it is foreseen that in the studies where the number of data is high, high performance will be achieved in the use of deep algorithms.

Anahtar Kelimeler

Civil Engineering; Classification; Deep Learning Algorithms; Deep Neural Network; Vision.

İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

Öz

Bu çalışmada inşaat mühendisliği alanına derin öğrenme algoritmalarının uygulanabilirliği araştırılmıştır. Öncelikle derin öğrenme algoritmaları hakkında bilgiler verilmiştir. Ayrıca inşaat mühendisliği alanında sınıflandırma, tahmin ve yorumlama gibi konularda yapılan derin öğrenme uygulamaları incelenmiştir. Uygulamalar inşaat mühendisliğinin ulaştırma, hidrolik, mekanik, geoteknik ve yapı alt bilim dallarına göre detaylandırılmıştır. Gerçekleştirilen uygulamaların başarı oranları üzerinden inşaat mühendisliğine katkıları analiz edilmiştir. Çalışmanın sonucunda veri sayısının fazla olduğu çalışmalarda derin öğrenme algoritmalarının kullanımında yüksek başarım elde edileceği ön görülmektedir.

Anahtar Kelimeler

Derin Öğrenme Algoritmaları; Derin Sinir Ağı; İnşaat mühendisliği; Sınıflandırma; Görü.

Kaynakça

• [1] Lakot A., E., and Şensoy, A., S., (2019). Mimarlık ve İnşaat Mühendisliği Arakesitinde Disiplinler Arası Eğitimin Önemi. Artium, 7(2), 82-90. [2] Gindis, E.J. and Kaebisch, R.C., 2020. Up and Running with AutoCAD 2020 2D Drafting and Design, Elsevier, Academic Press. 568s. [3] Lambropoulos, S., Pantouvakis, J-P., Marinelli, M., 2014. Reforming Civil Engineering Studies in Recession Times, 27th IPMA World Congress, Procedia - Social and Behavioral Sciences 119, 776 – 785, DOI: doi.org /10.1016/j.sbspro.2014.03.087. [4] Kurz, G., and Weber, R.F., 1983. International Exchange of Computer Programs for Engineering Education, Computers &Education, 7(2), 121-124. DOI: doi.org /10.1016/0360-1315(83)90023-4 [5] Economopoulos, V.P., 2009. ECCE President’s Introduction, Civil Engineering Heritage in Europe 18th - 21st Centruy, ECCE European Council of Civil Engineers, http://www.ecceengineers.eu/news/2018/ECCE_CEHE_book.pdf?m=1510575837& [6] Smallwood, J.J., and Wium, J., 2015. Mass and Density of Materials: Civil Engineering Students’ Knowledge and Perceptions. 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, Procedia Manufacturing 3, 6408 – 6413, DOI: doi.org /10.1016/j.promfg.2015.07.91 [7] Radzi, N.M., Mohamad, S., Abu, M.S., Phang, F.A., 2012. Are Math-Oriented Critical Thinking Elements in Civil Engineering Workplace Problems Significant? Insights from Preliminary Data and Analysis, International Conference on Teaching and Learning in Higher Education (ICTLHE 2012) in conjunction with RCEE & RHED 2012, Procedia - Social and Behavioral Sciences 56, 96 – 107. DOI: doi.org /10.1016/j.sbspro.2012.09.636 [8] Danielsen, D.A., Torp, O., Lohne, J., 2017. HSE in Civil Engineering Programs and Industry Expectations, Creative Construction Conference 2017, CCC 2017, 19-22 June 2017, Primosten, Croatia, Procedia Engineering 196 (2017) 327 – 334. DOI: doi.org /10.1016/j.proeng.2017.07.207. [9] Fröbel, T., Firmenich, B., Koch, C., 2011. Quality Assessment of Coupled Civil Engineering Applications, Advanced Engineering Informatics 25, 625–639. DOI: doi.org /10.1016/j.aei.2011.08.005 [10] Meža, S., Turk, Ž., Dolenc, M., 2015. Measuring the Potential of Augmented Reality in Civil Engineering, Advances in Engineering Software 90, 1–10. DOI: doi.org /10.1016/j.advengsoft.2015.06.005 [11] Goldberg, D.E. & Holland, J.H. Machine Learning (1988) 3: 95. DOI: doi.org /10.1023/A:1022602019183 [12] Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106. [13] Harvey, P. D. (1994). The impact of condom prices on sales in social marketing programs. Studies in family planning, 52-58. [14] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436. [15] Zhang, X., & Dai, H. (2019). Significant Wave Height Prediction with the CRBM-DBN Model. Journal of Atmospheric and Oceanic Technology, 36(3), 333-351. [16] Michielli, N., Acharya, U. R., & Molinari, F. (2019). Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in biology and medicine, 106, 71-81. [17] Rahman, M. H., Himawan, I., Sridharan, S., & Fookes, C. (2019, May). Investigating Domain Sensitivity of DNN Embeddings for Speaker Recognition Systems. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5811-5815). IEEE. DOI: doi.org /10.1109/ICASSP.2019.8683683 [18] Wang, Q., Guo, Y., Yu, L., & Li, P. (2017). Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Transactions on Emerging Topics in Computing. [19] Fandango, A., & Kapoor, A. (2018, April). Investigation of Iterative and Direct Strategies with Recurrent Neural Networks for Short-Term Traffic Flow Forecasting. In International Conference on Advances in Computing and Data Sciences (pp. 433-441). Springer, Singapore. [20] Fu, X., Fang, B., Qian, J., Wu, Z., Zhu, J., & Du, T. (2019, February). Roadside Traffic Sign Detection Based on Faster R-CNN. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing (pp. 439-444). ACM. [21] Derakhshani, A., & Foruzan, A. H. (2019). Predicting the principal strong ground motion parameters: A deep learning approach. Applied Soft Computing, 80, 192-201. [22] Wu, Z., Swietojanski, P., Veaux, C., Renals, S., & King, S. (2015). A study of speaker adaptation for DNN-based speech synthesis. In Sixteenth Annual Conference of the International Speech Communication Association. [23] Alpaydin, G. (2018). An Adaptive Deep Neural Network for Detection, Recognition of Objects with Long Range Auto Surveillance. In ICSC (pp. 316-317). [24] Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps (pp. 323-350). Springer, Cham. [25] Toman, M., Meltzner, G. S., & Patel, R. (2018). Data Requirements, Selection and Augmentation for DNN-based Speech Synthesis from Crowdsourced Data. In Interspeech (pp. 2878-2882). [26] Gulgec, N. S., Takáč, M., & Pakzad, S. N. (2019). Convolutional neural network approach for robust structural damage detection and localization. Journal of Computing in Civil Engineering, 33(3), 04019005. [27] Le Cun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I.., Henderson, D., Howard, R.E., & Hubbard, W. (1989). Handwritten digit recognition: Applications of neural network chips and automatic learning. IEEE Communications Magazine, 27(11), 41-46. DOI: doi.org /10.1109/35.41400 [28] Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211. [29] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. [30] Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems (pp. 802-810). [31] Yang, H., Shen, S., Yao, X., Sheng, M., & Wang, C. (2018). Competitive deep-belief networks for underwater acoustic target recognition. Sensors, 18(4), 952. [32] Li, Y., Nie, X., & Huang, R. (2018). Web spam classification method based on deep belief networks. Expert Systems with Applications, 96, 261-270. [33] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. [34] Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors, 19(18), 4021.DOI: doi.org /10.3390/s19184021 [35] Weber, T., Ercelik, E., Ebert, M., & Knoll, A. (2019, October). Recognition & Evaluation of Additional Traffic Signs on the example of’80 km/h when wet’. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 4134-4139). IEEE. [36] Sandhiyaa, S., & Vijayan, M. (2019). Traffic light and sign detection for autonomous land vehicle using raspberry Pi. Traffic, International Journal of Scientific Research and Review, 7(03). [37] Walch, M., Colley, M., & Weber, M. (2019, April). CooperationCaptcha: On-The-Fly Object Labeling for Highly Automated Vehicles. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (p. LBW0289). ACM. DOI: doi.org /10.1145/3290607.3313022 [38] Zhu, Z., Xu, G., He, H., Jiang, J., & Wang, T. (2019, April). Recognition of Speed Signs in Uncertain and Dynamic Environments. In Journal of Physics: Conference Series (Vol. 1187, No. 4, p. 042066). IOP Publishing. DOI: doi.org /10.1088/1742-6596/1187/4/042066 [39] Salti, S., Petrelli, A., Tombari F., Fioraio, N., and Stefano, L. D., “Traffic sign detection via interest region extraction,” Pattern Recognition, vol. 48, no. 4, pp. 1039–1049, 2015. 2 [40] Greenhalgh, J., and Mirmehdi, M., “Real-Time Detection and Recognition of Road Traffic Signs,” Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1498–1506, 2012. [41] Bangquan, X., & Xiong, W. X. (2019). Real-Time Embedded Traffic Sign Recognition Using Efficient Convolutional Neural Network. IEEE Access, 7, 53330-53346. DOI: doi.org /10.1109/ACCESS.2019.2912311 [42] Tabernik, D., & Skočaj, D. (2019). Deep Learning for Large-Scale Traffic-Sign Detection and Recognition. IEEE Transactions on Intelligent Transportation Systems, 1 – 14. DOI: doi.org /10.1109/TITS.2019.2913588 [43] Wu, L., Li, H., He, J., & Chen, X. (2019). Traffic sign detection method based on Faster R-CNN. In Journal of Physics: Conference Series (Vol. 1176, No. 3, p. 032045). IOP Publishing. DOI: doi.org /10.1088/1742-6596/1176/3/032045 [44] Jose, A., Thodupunoori, H., & Nair, B. B. (2019). A Novel Traffic Sign Recognition System Combining Viola–Jones Framework and Deep Learning. In Soft Computing and Signal Processing (pp. 507-517). Springer, Singapore. [45] Wu, C. C., Lin, Y. X., Hu, D. X., Ko, C. C., & Jiang, J. H. (2019). The Warning System for Speed Cameras on the Road by Deep Learning. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 768-775). Springer, Cham. [46] Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., & Song, D. (2018). Robust physical-world attacks on deep learning visual classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1625-1634). [47] Sitawarin, C., Bhagoji, A. N., Mosenia, A., Mittal, P., & Chiang, M. (2018). Rogue signs: Deceiving traffic sign recognition with malicious ads and logos. arXiv preprint arXiv:1801.02780. [48] Huang, W., Huang, M., & Zhang, Y. (2018). Detection of traffic signs based on combination of gan and faster-rcnn. In Journal of Physics: Conference Series (Vol. 1069, No. 1, p. 012159). IOP Publishing. [49] Zeng, Y., Xu, X., Shen, D., Y. Fang and Xiao, Z., (2017). "Traffic Sign Recognition Using Kernel Extreme Learning Machines with Deep Perceptual Features," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1647-1653, June 2017. [50] Cha Y., L., & Choi W., Öztürk, O., B., Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks, Computer-Aided Civil and Infrastructure Engineering 32 (2017) 361–378. [51] Gao, Y., and Mosalam K., M., Deep Transfer Learning for Image-Based Structural Damage Recognition, Computer-Aided Civil and Infrastructure Engineering 33 (2018) 748–768 [52] Zhang A., Wang, C. P. W., Li, B., Yang, E., Dai, X., Fei, Y., Liu,Y. Li, J., & Chen, C., (2017). Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network, Computer-Aided Civil and Infrastructure Engineering. 1–15. [53] Makantasis, K., Protopapadakis E., Doulamis A., Doulamis N., Loupos C., Deep Convolutional Neural Networks for Efficient Vision Based Tunnel Inspection, 015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, 2015, pp. 335-342 [54] Mohtasham K., M., Vahidnia, S., Ghasemzadeh, L., Ozturk, Y. E., Yuvalaklioglu, M., Akin, S., & Ure, N. K. (2019). Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines. Structural Health Monitoring, DOI: doi.org/10.1177/1475921719883202. [55] Ye, X. W., Jin, T., & Chen, P. Y. (2019). Structural crack detection using deep learning–based fully convolutional networks. Advances in Structural Engineering. 22(16) 3412–3419. DOI: doi.org /10.1177/1369433219836292 [56] Fan, X., Li, J., Hao, H., & Ma, S. (2018). Identification of minor structural damage based on electromechanical impedance sensitivity and sparse regularization. Journal of Aerospace Engineering, 31(5), 04018061. [57] Abdeljaber, O., Avci, O., Kiranyaz, M. S., Boashash, B., Sodano, H., & Inman, D. J. (2018). 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing, 275, 1308-1317. [58] Modarres, C., Astorga, N., Droguett, E. L., & Meruane, V. (2018). Convolutional neural networks for automated damage recognition and damage type identification. Structural Control and Health Monitoring, 25(10), e2230.DOI: doi.org /10.1002/stc.2230 [59] Cha, Y. J., Choi, W. & Büyüköztürk, O. (2017), Deep learning‐based crack damage detection using convolutional neural networks, Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361–78. [60] Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Systems with Applications, 118, 425-439. [61] Mackenzie, J., Roddick, J. F., & Zito, R. (2018). An evaluation of HTM and LSTM for short-term arterial traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1847-1857. [62] Jia, Y., Wu, J., & Du, Y. (2016). Traffic speed prediction using deep learning method. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1217-1222. DOI: doi.org /10.1109/ITSC.2016.7795712 [63] Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y., 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp.Res. Part C Emerg. Technol.s 54, 187–197. [64] 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. [65] Wang Q., Guo Y., Yu L., Pan L., 2017. Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. Transactions on Emerging Topics in Computing, pp. 2168-6750. [66] Fu, R., Zhang, Z. and L. Li, "Using LSTM and GRU neural network methods for traffic flow prediction," 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, 2016, pp. 324-328. DOI: doi.org/ 10.1109/YAC.2016.7804912 [67] Wu, Y., & Tan, H. (2016). Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022. [68] 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. [69] Luo, X., Li, D., Yang, Y., and Zhang, S., “Spatiotemporal Traffic Flow Prediction with KNN and LSTM,” Journal of Advanced Transportation, vol. 2019, Article ID 4145353, 10 pages, 2019. DOI: doi.org /10.1155/2019/4145353. [70] Zheng, Z., Yang, Y., Liu, J., Dai, H. N., & Zhang, Y. (2019). Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics. IEEE Transactions on Intelligent Transportation Systems. Doi: 10.1109/TITS.2019.2909904 [71] Yang, D., Chen, K., Yang, M., & Zhao, X. (2019). Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features. IET Intelligent Transport Systems, 13(10), 1475-1482. DOI: doi.org /10.1049/iet-its.2018.5511 [72] Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926. [73] Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166-180. [74] Zang, D., Ling, J., Wei, Z., Tang, K., & Cheng, J. (2018). Long-Term Traffic Speed Prediction Based on Multiscale Spatio-Temporal Feature Learning Network. IEEE Transactions on Intelligent Transportation Systems. DOI: doi.org/10.1109/TITS.2018.2878068 [75] Wang, J., Chen, R., & He, Z. (2019). Traffic speed prediction for urban transportation network: A path based deep learning approach. Transportation Research Part C: Emerging Technologies, 100, 372-385. [76] Chu, K. F., Lam, A. Y., & Li, V. O. (2018, November). Travel Demand Prediction using Deep Multi-Scale Convolutional LSTM Network. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1402-1407). IEEE. [77] Sun, Y., Jiang, G., Lam, S. K., Chen, S., & He, P. (2019, May). Bus Travel Speed Prediction using Attention Network of Heterogeneous Correlation Features. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 73-81). Society for Industrial and Applied Mathematics. [78] Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019). Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Systems with Applications, 120, 426-435. [79] Das, S., Kalava, R. N., Kumar, K. K., Kandregula, A., Suhaas, K., Bhattacharya, S., & Ganguly, N. (2019). Map Enhanced Route Travel Time Prediction using Deep Neural Networks. arXiv preprint arXiv:1911.02623. [80] Huang L., Li, J., Hao, H., and Li, X., (2018), Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning, Tunnelling and Underground Space Technology, 81 (2018) 265–276. DOI: doi.org/10.1016/j.tust.2018.07.006 [81] Dickey, J., Borghetti, B., & Junek, W. (2019). Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog. Sensors, 19(3), 597. DOI: doi.org/10.3390/s19030597 [82] Zhu, L., Peng, Z., McClellan, J., Li, C., Yao, D., Li, Z., & Fang, L. (2019). Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7. 9 Wenchuan Earthquake. Physics of the Earth and Planetary Interiors. 293 (2019) 106261. DOI: doi.org/10.1016/j.pepi.2019.05.004 [83] Dung, C. V. (2019). Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99, 52-58. [84] Kuyuk, H. S., & Susumu, O. (2018). Real-Time Classification of Earthquake using Deep Learning. Procedia Computer Science, 140, 298-305. [85] Meng, R., Rice, S. G., Wang, J., & Sun, X. (2018). A fusion steganographic algorithm based on faster R-CNN. Computers, Materials & Continua, 55(1), 1-16. DOI: doi.org/10.3970/cmc.2018.055.001 [86] Sinardet, D., & Mortelmans, D. (2006). Between Al-Jazeera and CNN: Indicators of media use by Belgian ethnic minority youth. Communications, 31(4), 425-445. DOI: doi.org/10.1515/COMMUN.2006.027 [87] Dominguez-Sanchez, A., Cazorla, M., & Orts-Escolano, S. (2017). Pedestrian movement direction recognition using convolutional neural networks. IEEE transactions on intelligent transportation systems, 18(12), 3540-3548. DOI: doi.org/10.1109/TITS.2017.2726140 [88] Al-Qizwini, M., Barjasteh, I., Al-Qassab, H., & Radha, H. (2017, June). Deep learning algorithm for autonomous driving using googlenet. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 89-96). IEEE. DOI: doi.org/10.1109/IVS.2017.7995703 [89] Buslaev, A., Seferbekov, S. S., Iglovikov, V., & Shvets, A. (2018, June). Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery. In CVPR Workshops (pp. 207-210). [90] Xue, Y., & Li, Y. (2018). A fast detection method via region‐based fully convolutional neural networks for shield tunnel lining defects. Computer‐Aided Civil and Infrastructure Engineering, 33(8), 638-654. [91] Ho-Kun, J., Jonathan, E., Seungryong, K., & Chan-Su, Y. (2019). Sea Fog Classification from GOCI Images using CNN Transfer Learning Models. IEICE Technical Report; IEICE Tech. Rep., 119(255), 87-90. [92] Mangalathu, S., & Burton, H. V. (2019). Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions. International Journal of Disaster Risk Reduction, 36, 101111. [93] Zhong, B., Xing, X., Love, P., Wang, X., & Luo, H. (2019). Convolutional neural network: Deep learning-based classification of building quality problems. Advanced Engineering Informatics, 40, 46-57. [94] Wen, C., Sun, X., Li, J., Wang, C., Guo, Y., & Habib, A. (2019). A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds. ISPRS journal of photogrammetry and remote sensing, 147, 178-192. [95] Ni, X., Wang, H., Che, C., Hong, J., & Sun, Z. (2019). Civil aviation safety evaluation based on deep belief network and principal component analysis. Safety science, 112, 90-95. [96] Beckman, G. H., Polyzois, D., & Cha, Y. J. (2019). Deep learning-based automatic volumetric damage quantification using depth camera. Automation in Construction, 99, 114-124. [97] Bentes, C., Velotto, D., & Tings, B. (2017). Ship classification in terrasar-x images with convolutional neural networks. IEEE Journal of Oceanic Engineering, 43(1), 258-266. DOI: doi.org/10.1109/JOE.2017.2767106 [98] Li, T., Zhang, J., & Zhang, Y. (2014, October). Classification of hyperspectral image based on deep belief networks. In 2014 IEEE international conference on image processing (ICIP) (pp. 5132-5136). IEEE. DOI: doi.org/10.1109/ICIP.2014.7026039 [99] Buscombe, D., & Ritchie, A. (2018). Landscape classification with deep neural networks. Geosciences, 8(7), 244.