Artificial Intelligence-based integration model for design, construction, and health monitoring of building structures

국문 초록 (Abstract)

인공지능은 인식, 학습, 추론, 문제해결, 최적화와 같은 지능적 행동을 모사하는 컴퓨터 과학의 한 분야로서, 최근 건축 공학의 설계, 시공, 구조 건전도 모니터링 분야에서도 관련 연구가 활발하게 수행되고 있다. 다수의 주목할만한 성과들이 문헌상 보고되고 있지만, 여전히 해결되지 않은 문제들이 존재하기 때문에, 본 연구는 폭넓은 문헌 조사를 통해 기존 연구의 한계를 확인하고, 이를 해결하기 위한 인공지능 기반 통합 모델을 제안한다.
기존 연구의 한계는 다음과 같다. 설계 분야에서는 구조 최적설계와 관련하여, 다중 에이전트 최적화 방법이 높은 계산 비용 문제를 갖고 있다. 단일 에이전트 최적화 방법도 제약 조건의 효과적 관리가 어렵다는 한계를 갖는다. 시공 분야에서는 영상기반 치수 품질검사와 관련하여, 촬영 환경 변화 민감성, 참조 표적 의존성, 카메라 캘리브레이션 의존성, 특징 매칭 및 삼차원 재구성 과정에서 수반되는 오류, 높은 계산 요구 등의 한계가 보고되고 있다. 구조 건전도 모니터링 분야에는 기존의 비파괴 검사 기반 콘크리트 압축강도 추정식들의 정확도가 높지 않고, 영상기반 장기간 구조 건전도 모니터링 연구가 부족하다는 한계가 있다.
이러한 문제를 해결하기 위하여, 통합 모델은 다음 여섯 개의 하위모델로 구성된다. 설계 분야에서는, 트러스 구조물의 크기, 형상, 및 위상을 동시에 최적화 할 수 있는 하위 모델 A가 개발된다. 이는 단일 에이전트 최적화 방법에 기반하여, 그래디언트 클리핑, 그래디언트 강제 조정, 리키렐루 기반의 페널티 함수를 도입함으로써, 최적화 과정에서 계산비용을 줄이는 동시에 제약조건의 효과적 관리를 가능하게 한다. 적용성은 캔틸레버 및 거더 트러스 예제에 대한 시뮬레이션을 통해 검증된다.
시공 분야에서는 건설 치수 품질 검사를 위한 세 가지 하위 모델이 개발된다. 하위 모델 B는 사진으로부터 부재 단면도를 자동으로 재구성할 수 있는 모델로서, 건설 부재 치수 품질 검사에 이용될 수 있다. 이는 카메라 위치와 자세를 추정하고 복원하는 과정에 역 원근 투영 최적화 방법을 도입함으로써, 카메라 캘리브레이션, 특징 매칭, 참조 표적에 의존하지 않는다. 재구성 과정은 딥러닝 모델에 의해 완전 자동화되며, 모델의 강건성 확보를 위한 가상 훈련 데이터 생성 방법이 함께 소개된다. 적용성은 세 가지 환경 조건에서의 단면도 복원 실험을 통해 검증된다. 하위 모델 C는 이미지 해상도를 가로, 세로 각 16배 증가시킬 수 있는 이미지 초해상화 모델로서, 산업 현장에서 저해상도 카메라를 이용한 치수 검사가 가능하게 할 수 있다. 하위 모델 C의 적용성은 건설 부재 단면에 대한 초해상화 실험을 통해 검증된다. 하위 모델 D는 드론 촬영된 단일 이미지로부터 건설 현장의 철근 배근 치수 품질을 검사할 수 있는 모델이다. 이는 사진 측량학의 전통적 기술들과, 입자 군집 최적화를 이용하여, 카메라의 위치와 자세를 추정하고, 이를 통해 원근 왜곡이 보정된 정사 영상을 생성하여 치수 검사에 이용한다. 때문에 특징 매칭, 삼차원 재구성이 불필요하며, 방법론이 간결하고, 환경 변화에 강건한 성능을 갖는다. 적용성은 실제 아파트 건설 현장에 대한 적용 실험을 통해 검증되었다.
구조 건전도 모니터링 분야에서는 두 가지 하위 모델이 개발된다. 하위 모델 E는 비파괴 검사 결과로부터 콘크리트 압축강도를 추정하는, 정확도가 개선된 추정식을 도출하는 모델로서, 맞춤형 인공신경망과 유전자 알고리즘을 이용한다. 적용성은 선행 연구 문헌에서 수집된 11개의 실험 데이터에 기반하여 검증된다. 하위 모델 F는 영상에 기반하여 계측되는 건물의 최상부 두개 층 좌표 정보로부터, 건물의 변형률 분포를 예측할 수 있는 모델로서, 합성곱 신경망을 이용한다. 적용성은 축소된 철골 구조물 실험을 통해 검증된다.
결론적으로, 본 연구는 전문가 시스템, 유전자 알고리즘, 입자 군집 최적화, 경사 하강법, 다층 퍼셉트론, 합성곱 신경망, 적대적 생성 신경망, 그리고 기존 컴퓨터 비전 기술을 포함하는 다양한 기술을 융합 활용함으로써, 설계, 시공, 구조 건전도 모니터링 분야의 기존 연구 한계를 해결하였다. 이는 인공지능 기술이 매핑 문제로 다루어질 수 있는 건축 공학의 다른 복잡한 문제들 에도 효과적일 수 있음을 시사한다.

다국어 초록 (Multilingual Abstract)

Artificial intelligence (AI), a branch of computer science, aims to replicate intelligent behaviors such as recognition, learning, inference, problem-solving, and optimization. In recent decades, the application of AI in building engineering has gained significant attention, particularly in the domains of design, construction, and health monitoring. While notable advancements have been achieved, many challenges remain unresolved. This dissertation aims to address these persistent issues by proposing an integration model. Through a comprehensive literature review, the study identified key challenges, and the integration model comprises six submodels, designated as Models A to F.
The limitations addressed by this integration model span several critical areas. In building structural design, current multi-agent optimization methods are hindered by high computational costs, while single-agent methods struggle to manage constraints effectively. In construction, vision-based dimensional quality inspection is limited by sensitivity to environmental changes, dependence on reference markers and fixed camera setups, inaccuracies arising from feature matching, challenges with 3D reconstruction, and issues with computational efficiency. For health monitoring, existing equational models for estimating concrete concrete compressive strength using non-destructive tests often lack accuracy. Additionally, vision-based models for long-term monitoring of structural responses are still underdeveloped.
To address these limitations, the proposed integration model incorporates six distinct submodels. In design domain, Model A is introduced to optimize the size, shape, and topology of truss structures. This submodel utilizes a single-agent gradient descent method with gradient adjustment and clipping techniques, combined with a leaky rectified linear unit-based penalty function to effectively handle constraints. Its applicability is demonstrated through simulations of cantilever and girder trusses. In the construction domain, Models B, C, and D have been developed. Model B enables the automated reconstruction of cross-section drawings for the dimensional quality inspection of construction components. By utilizing deep learning models and inverse perspective projection optimization, this submodel eliminates the need for camera calibration, reference markers, and feature matching. Its robustness is further enhanced through synthetic image generation, and its performance is validated under three distinct environmental conditions. Model C focusses on enhancing image resolution by a factor of 16, thereby enabling the effective use of low-resolution cameras for dimensional quality inspection. Validation experiments demonstrate its superior performance compared to a baseline model. Model D addresses rebar placement inspection by leveraging photogrammetry and particle swarm optimization. By generating orthographic images from the single drone-captured photographs, this submodel eliminates the need for feature matching and 3D reconstruction while ensuring robust performance. Its effectiveness is validated in an actual construction site setting. For health monitoring, Models E and F have been introduced. Model E integrates a customized neural network with a genetic algorithm to develop an improved estimation equation for concrete compressive strength. The neural network enhances accuracy through its nonlinear mapping capabilities, while the genetic algorithm refines the equation. This submodel is validated using 11 experimental datasets from existing literature. Model F employs a convolutional neural network to predict strain distribution using the corner coordinates of the top two floors of a building. Its potential is demonstrated through experiments on a scaled steel frame specimen.
This dissertation contributes to the advancement of design, construction, and health monitoring in building engineering by combining various types of AI technologies, such as expert systems, genetic algorithm, particle swarm optimization, gradient descent, deep neural networks, and computer vision. Furthermore, it suggests the potential applicability of AI to other challenging problems in building engineering that can be addressed as mapping problems.

목차 (Table of Contents)

• LIST OF FIGURES v
• LIST OF TABLES x
• ABSTRACT xi
• 1. INTRODUCTION 1
• 1.1. Research Background 1
• 1.2. Dissertation Objectives and Scope 4
• 1.3. Dissertation Structure 8
• 2. LITERATURE REVIEW 9
• 2.1. Artificial Intelligence 9
• 2.1.1. Overview 9
• 2.1.2. Expert Systems 11
• 2.1.3. Optimization 12
• 2.1.4. Machine Learning 14
• 2.1.5. Computer Vision 16
• 2.1.6. Natural Language Processing 17
• 2.1.7. Summary 18
• 2.2. Artificial Intelligence in Building Engineering 20
• 2.2.1. Overview 20
• 2.2.2. Artificial Intelligence-Based Design in Building Engineering (Focusing on Structural Design) 21
• 2.2.3. Artificial Intelligence-Based Construction (Focusing on Dimensional Quality Inspection in Construction) 25
• 2.2.4. Artificial Intelligence-Based Structural Health Monitoring 28
• 2.2.5. Summary 30
• 3. METHODOLOGY 35
• 3.1. Overview 35
• 3.2. Model A: Single-Agent Optimization Model for Size, Shape, and Topology of Plane Truss Structures 36
• 3.2.1. Introduction to Model A 36
• 3.2.2. Operation Flowchart of Model A 38
• 3.2.3. Details of Model A 41
• 3.2.3.1. Optimization Problem Formulation 41
• 3.2.3.2. Process Description 44
• 3.3. Model B: Stereo Vision-Based Dimensional Inspection Model for Construction Component Cross-Sections Based on Faster Region-Based Convolutional Neural Network and Pixel-to-Pixel Generative Adversarial Network 59
• 3.3.1. Introduction to Model B 59
• 3.3.2. Operation Flowchart of Model B 62
• 3.3.3. Details of Model B 63
• 3.4. Model C: 16× Image Super-Resolution Model for Enhancing Measurement Precision Based on Super-Resolution Generative Adversarial Network 72
• 3.4.1. Introduction to Model C 72
• 3.4.2. Details of Model C 72
• 3.5. Model D: Vision-Based Dimensional Inspection Model for Rebar Placement Using Particle Swarm Optimization 74
• 3.5.1. Introduction to Model D 74
• 3.5.2. Operation Flowchart of Model D 75
• 3.5.3. Details of Model D 77
• 3.5.3.1. Particle Swarm Optimization 77
• 3.5.3.2. Optimization Problem Formulation 80
• 3.5.3.3. Process Description 81
• 3.6. Model E: Equation Derivation Model for Evaluating Concrete Compressive Strength of Existing Buildings Using a Customized Neural Network and Genetic Algorithm 88
• 3.6.1. Introduction to Model E 88
• 3.6.2. Operation Flowchart of Model E 90
• 3.6.3. Details of Model E 91
• 3.7. Model F: Building Strain Prediction Model from Top-Two-Floor Corner Coordinates Using a Coordinate Map and Convolutional Neural Network 101
• 3.7.1. Introduction to Model F 101
• 3.7.2. Operation Flowchart of Model F 102
• 3.7.3. Details of Model F 104
• 4. IMPLEMENTATION 108
• 4.1. Overview 108
• 4.2. Model A: Single-Agent Optimization Model for Size, Shape, and Topology of Plane Truss Structures 108
• 4.2.1. Example A: Cantilever Truss 108
• 4.2.2. Example B: Girder Truss 116
• 4.3. Model B: Stereo Vision-Based Dimensional Inspection Model for Construction Component Cross-Sections Based on Faster Region-Based Convolutional Neural Network and Pixel-to-Pixel Generative Adversarial Network 123
• 4.3.1. Data Acquisition 123
• 4.3.2. Model Training 125
• 4.3.3. Experimental Setup 125
• 4.3.4. Experimental Results 126
• 4.4. Model C: 16× Image Super-Resolution Model for Enhancing Measurement Precision Based on Super-Resolution Generative Adversarial Network 134
• 4.4.1. Data Acquisition 134
• 4.4.2. Model Training 135
• 4.4.3. Experimental Setup 136
• 4.4.4. Experimental Results 136
• 4.5. Model D: Vision-Based Dimensional Inspection Model for Rebar Placement Using Particle Swarm Optimization 141
• 4.5.1. Experimental Setup 141
• 4.5.2. Experimental Results 142
• 4.6. Model E: Equation Derivation Model for Evaluating Concrete Compressive Strength of Existing Buildings Using a Customized Neural Network and Genetic Algorithm 152
• 4.6.1. Data Acquisition 152
• 4.6.2. Model Training and Derivation of Estimation Equation 159
• 4.6.3. Performance Evaluation of the Derived Estimation Equation 163
• 4.6.4. Additional Analysis 166
• 4.7. Model F: Building Strain Prediction Model from Top-Two-Floor Corner Coordinates Using a Coordinate Map and Convolutional Neural Network 169
• 4.7.1. Experimental Setup and Data Acquisition 169
• 4.7.2. Model Training 171
• 4.7.3. Experimental Results 172
• 4.7.4. Additional Analysis 173
• 5. CONCLUSION 179
• REFERENCES 182
• APPENDIX 1 – LIST OF ABBREVIATIONS 211
• APPENDIX 2 – LIST OF SYMBOLS 215
• ABSTRACT IN KOREAN 228

참고문헌 (Reference)

1. Deep learning, Lecun, Y., Bengio, Y., & Hinton, G., 521(7553), 436-444, , 2015

2. Competitive gradient descent, Schäfer, F., & Anandkumar, A, 32, , 2019

3. Model-based machine learning, Bishop, C. M., 371(1984), 20120222, , 2013

4. A survey on vision transformer, Zhang, Y., Chen, H., Han, K., Wang, Y., Tang, Y., Xiao, A., Tao, D., Chen, X., Yang, Z., Xu, Y., Liu, Z., Xu, C., Guo, J., 45(1), 87-110, , 2023

5. Generative Adversarial Networks, Bengio, Y., Goodfellow, I., Ozair, S., Xu, B., Courville, A., Warde-Farley, D., Pouget-Abadie, J., Mirza, M., 63(11), 139-144, , 2020

6. Speeded-Up Robust Features (SURF), Ess, A., Tuytelaars, T., Bay, H., Van Gool, L., 110(3), 346-359, , 2008

7. Computer vision: The last 50 years, Shapiro, L. G., International Journal of Parallel, Emergent and Distributed Systems, 35(2), 112-117, , 2020

8. Deep neural networks in psychiatry, Durstewitz, D., Koppe, G., Meyer-Lindenberg, A, 24(11), 1583-1598, , 2019

9. The turing test: The first 50 years, French, R. M., 4(3), 115- 122, , 2000

10. Expert systems for structural design, Maher, M. L., 1(4), 270-283, , 1987

11. Machine learning for microbiologists, Passerini, A., Asnicar, F., Thomas, A. M., Waldron, L., Segata, N., 22(4), 191-205, , 2024

12. Metaheuristics: Review and application, Gogna, A., & Tayal, A, 25(4), 503-526, , 2013

13. Reinforcement learning: an introduction, Barto, A. G., Sutton, R. S., MIT press, , 2018

14. Optimization of large structural systems, Rozvany, G. I, Vol. 231, , 2013

15. Microsoft COCO: Common objects in context, Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L., Part V 13, , 2014

16. The way forward with ai-complete problems, Groppe, S., Jain, S., 42(1), 1-5, , 2024

17. A Computational Approach to Edge Detection, Canny, J., (6), 679-698, , 1986

18. A new optimizer using particle swarm theory, Kennedy, J., Eberhart, R., 4-6 MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, , 1995

19. Deep Reinforcement Learning: A Brief Survey, Brundage M, Deisenroth MP, Bharath AA, Arulkumaran K, 34(6), 26-38, , 2017

20. Deep learning for 3d point clouds: A survey, Wang, H., Liu, L., Liu, H., Bennamoun, M., Hu, Q., Guo, Y., 43(12), 4338- 4364, , 2021

21. Deep residual learning for image recognition, Zhang, X., Ren, S., He, K., Sun, J., Proceedings of the IEEE conference on computer vision and pattern recognition, , 2016

22. Generative ai design for building structures, Huang, Y., Liao, W., Fei, Y., Lu, X., Gu, Y., Automation in Construction, 157, 105187, , 2024

23. Ai-based structural health monitoring systems, Keshmiry, A., Hassani, S., Dackermann, U., In Artificial intelligence applications for sustainable construction (pp. 151-170, , 2024

24. A flexible new technique for camera calibration, Zhang, Z, 22(11), 1330-1334, , 2000

25. Artificial intelligence: A promising technology, Hirsch-Kreinsen, H., 39(4), 1641-1652, , 2024

26. Characterization of a rs-lidar for 3d perception, Liao, Q., Wang, Z., Liu, Y., Wang, L., Liu, M., Ye, H., 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Whitehurst, E. A. Soniscope tests concrete structures 47(2)., , 2018

27. Structural design through reinforcement learning, Aage, N., Rochefort-Beaudoin, T., Vadean, A., Achiche, S., arXiv preprint arXiv:2407.07288, , 2024

28. Multiobjective optimization of seismic structures, Li, D., Ger, J., Cheng Franklin, Y., pp. 1-8). https://doi. org/doi:10.1061/40492(2000)24 10.1061/40492(2000)24, , 2012

29. Learning representations by backpropagating errors, Rumelhart, D. E., Hinton, G. E., & Williams, R. J, 323(6088), 533-536, , 1986

30. Feature extraction by using deep learning: A survey, Dara, S., Tumma, P., 29-31 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), , 2018

31. Design of an expert system architecture: An overview, Manjunatha, L., Janjanam, D., Ganesh, B., Journal of Physics: Conference Series, , 2021

32. Reconfusion: 3d reconstruction with diffusion priors, Poole, B., Srinivasan, P. P., Wu, R., Verbin, D., Mildenhall, B., Henzler, P., Gao, R., Park, K., Barron, J. T., Watson, D., Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, , 2024

33. Backpropagation and stochastic gradient descent method, Amari, S.-I, 5(4), 185-196, , 1993

34. Overview of the transformer-based models for nlp tasks, Gillioz, A., Khaled, O. A., Casas, J., Mugellini, E., 2020 15th Conference on Computer Science and Information Systems (FedCSIS), , 2020

35. Expert system for optimum design of concrete structures, Reddy, R. R.,, Gupta, A., Singh, R. P., 7(2), 146-161, , 1993

36. Advances in optimization of highrise building structures, Aldwaik, M., & Adeli, H., 50(6), 899-919, , 2014

37. Text feature extraction based on deep learning: A review, Gao, Y., Sun, Y., Liang, H., Sun, X., 2017(1), 211, , 2017

38. Why general artificial intelligence will not be realized, Fjelland, R., 7(1), 10, , 2020

39. A 3x3 isotropic gradient operator for image processing. a, Sobel, I., & Feldman, G., 1968, 271-272, , 1968

40. A review on structural health monitoring: Past to present, Katam, R., Pasupuleti, V. D. K., Kalapatapu, P., 8(9), 248, , 2023

41. Distributed genetic algorithm for structural optimization, Adeli, H., & Kumar, S., 8(3), 156-163, , 1995

42. Perspective correction method for chinese document images, Zhang, W., Li, X., & Ma, X, 21-22 Dec., , 2008

43. Real-time automatic crack detection method based on drone, Meng, S., Zhou, Y., Gao, Z., He, B., Djerrad, A., Computer-Aided Civil and Infrastructure Engineering, 38(7), 849-872, , 2023

44. What is machine learning? A primer for the epidemiologist, Bi, Q., Lessler, J., Kaminsky, J., Goodman, K. E., 188(12), 2222-2239, , 2019

45. Particle swarm approach for structural design optimization, Perez, R. E., & Behdinan, K., 85(19), 1579-1588, , 2007

46. An integrated classification model for incremental learning, Li, Z., Yan, C., Liu, X., Hu, J., Zhang, J., Peng, D., Yang, Y., Ren, C., 80(11), 17275- 17290, , 2021

47. Automatic correction of perspective and optical distortions, Salgado, A., Santana-Cedrés, D., Esclarín, J., Alvarez, L., Gomez, L., Mazorra, L., Alemán-Flores, M., 161, 1-10, , 2017

48. Few-shot object detection: Research advances and challenges, Ding, W., Shao, Y., You, X., Wu, T., Chen, S., Xin, Z., 107, 102307, , 2024

49. Integration of expert systems in a structural design office, Cauvin, A., Passera, R., & Stagnitto, G., 411-423, , 1998

50. Text mining and natural language processing in construction, Park, J. Y, Shamshiri, A., Ryu, K. R., 158, 105200, , 2024

51. A comprehensive survey of loss functions in machine learning, Ma, Y., Wang, Q., Zhao, K., Tian, Y., 9(2), 187-212, , 2022

52. A review of convolutional neural networks in computer vision, Zhang, Y., Deveci, M., Parmar, M., Han, X., Zhao, X., Wang, L., 57(4), 99, , 2024

53. Optimization of deep learning models: Benchmark and analysis, Alsmadi, I., Al-Ramahi, M., Ahmad, R., 3(2), 7, , 2023

54. The turing test and our shifting conceptions of intelligence, Mitchell, M., 385(6710), eadq9356, , 2024

55. Computer vision techniques in construction: A critical review, Ngo, T., Xu, S., Shou, W., Sadick, A.-M., Wang, X., Wang, J., 28(5), 3383-3397, , 2021

56. Conceptual: An expert system for conceptual structural design, Karshenas, S., Haber, D., Computer-Aided Civil and Infrastructure Engineering, 5(2), 119-127, , 1990

57. Exploiting reflection change for automatic reflection removal, He, S., Zhao, Y., Wu, Z., Hu, F., Gooi, H. B., Ding, Y., Wen, G., Li, Y., Li, Y., Geng, H., Brown, M. S., Duan, J., Zhang, Proceedings of the IEEE international conference on computer vision, , 2013

58. Cognito: Automated feature engineering for supervised learning, Khurana, U., Parthasrathy, S., Samulowitz, H., Turaga, D., 12-15 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW, , 2016

59. Concrete compressive strength using artificial neural networks, Asteris, P. G., Mokos, V. G., Neural Computing and Applications, 32(15), 11807-11826, , 2020

60. Lstm inefficiency in longterm dependencies regression problems, Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., & Muneer, A, 30(3), 16-31, , 2023

61. Movemo: A structured approach for engineering reward functions, Pardo, R., Pelliccione, P., Duplessis, V., Schneider, G., Mallozzi, P., 2018 Second IEEE International Conference on Robotic Computing (IRC), , 2018

62. Divas: Video and audio synchronization with dynamic frame rates, Fernandez-Labrador, C., Massich, J., Akçay, M., Abecassis, E., Schroers, C., Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, , 2024

63. Imagenet classification with deep convolutional neural networks, Krizhevsky, A., Sutskever, I., & Hinton, G. E., 60(6), 84-90, , 2017

64. Machine learning optimization algorithms & portfolio allocation, Roncalli, T., Perrin, S., Machine Learning for Asset Management: New Developments and Financial Applications, 261- 328, , 2020

65. Metaheuristic algorithms in smart farming: An analytical survey, Goel, L., Mishra, A., 41(1), 46-65, , 2024

66. A novel optimization approach for minimum cost design of trusses, Kripakaran, P., Gupta, A., & Baugh, J. W., 85(23), 1782-1794, , 2007

67. Image-to-image translation with conditional adversarial networks, Zhou, T., Zhu, J.-Y., Efros, A. A., Isola, P., Proceedings of the IEEE conference on computer vision and pattern recognition, , 2017

68. Machine learning in sports science: Challenges and opportunities, Delahunt, E., Richter, C., O’reilly, M., 23(8), 961-967, , 2024

69. Nlp-based recommendation approach for diverse service generation, Jeong, B., Lee, K. J., 12, 14260-14274, , 2024

70. Typical advances of artificial intelligence in civil engineering, Li, H., Xu, Y., Qian, W., Li, N., Advances in Structural Engineering, 25(16), 3405-3424, , 2022

71. Design of an image edge detection filter using the sobel operator, Kanopoulos, N., Vasanthavada, N., & Baker, R. L., 23(2), 358-367, , 1988

72. Artificial intelligence based optimization techniques: A review In, A. Soni, S. A. Siddiqui, A. Mundra, Swarnkar, A. A. Kalam, Swarnkar, A., K. R. Niazi, Intelligent Computing Techniques for Smart Energy Systems Singapore, , 2020

73. Emerging artificial intelligence methods in structural engineering, Salehi H, Burgueño R., 171, 170-189, , 2018

74. Convergence of gradient descent for learning linear neural networks, Nguegnang, G. M., Rauhut, H., & Terstiege, U, 2024(1), 23, , 2024

75. Multi-objective seismic design optimization of structures: A review, Zakian, P., Kaveh, A., 31(2), 579-594, , 2024

76. Nature inspired meta heuristic algorithms for optimization problems, H. S, A., S. S, V. C., Computing, 104(2), 251-269, , 2022

77. Underwater images contrast enhancement and its challenges: A survey, Almutiry, O., Iqbal, K., Hussain, S., Mahmood, A., & Dhahri, H., 83(5), 15125-15150, , 2024

78. A survey of deep neural network architectures and their applications, Liu, X., Liu, W., Wang, Z., Liu, Y., Alsaadi, F. E., Zeng, N., 234, 11-26, , 2017

79. A survey of optimization methods from a machine learning perspective, Cao, Z., Zhu, H., Zhao, J., Sun, S., IEEE Transactions on Cybernetics, 50(8), 3668-3681, , 2020

80. Impact of automatic feature extraction in deep learning architecture, Asafuddoula, M., Shaheen, F., Verma, B., 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), , 2016

81. Multi-objective modified heat transfer search for truss optimization, Bureerat, S., Tejani, G. G., Pholdee, N., Kumar, S., 37(4), 3439-3454, , 2021

82. Optimal design of truss-structures using particle swarm optimization, Luh, G.-C., & Lin, C.-Y, 89(23), 2221-2232, , 2011

83. Multiobjective optimization of concrete frames by simulated annealing, Paya, I., Yepes, V., González-Vidosa, F., & Hospitaler, A, 23(8), 596-610, , 2008

84. Review on automated quality inspection of precast concrete components, Liu, Y., Li, J., Ma, Z., Automation in Construction, 150, 104828, , 2023

85. Deep semi-supervised learning for medical image segmentation: A review, Sheng, V. S., Liu, Z., Han, K., Ma, S., Qiu, C., Liu, Y., Song, Y., 245, 123052, , 2024

86. Use of the hough transformation to detect lines and curves in pictures, Hart, P. E., Duda, R. O., Commun. ACM, 15(1), 11–15, , 1972

87. Accuracy of non-destructive evaluation of concrete compression strength, Nobile, L., Bonagura, M., The 12th International Conference of the Slovenian Society for Non-Destructive Testing, Portorož, Slovenia, , 2013

88. Augmenting high-dimensional nonlinear optimization with conditional gans, Lepech, M. D., Kalehbasti, P. R., Pandher, S. S., Proceedings of the Genetic and Evolutionary Computation Conference Companion Lille, France, , 2021

89. Hybrid artificial intelligence optimization technique In A. Azizi (Ed.),, Azizi, A., of techniques in industry 4.0 (pp. 27-47 Springer Singapore. https://doi. org/10.1007/978-981-13-2640-0_4, , 2019

90. Prefabricated concrete component geometry deviation statistical analysis, Long, H., Luo, X., Wu, J., Dong, S., 2021(1), 9993451, , 2021

91. The evolution of lidar and its application in high precision measurement, Guo, K., Luo, S., Pan, H., Wang, X., Yang, X., IOP Conference Series: Earth and Environmental Science, , 2020

92. Applications of ga and hybrid ga in various domains: Design and prospects, Darius, P. S. H., Devadason, J. R., & Solomon, D. G., 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS, , 2024

93. Estimation of concrete strength by combined nondestructive testing method, Tanigawa, Y., Baba, K., & Mori, H., 82, 57-76, , 1984

94. Machine-learning applications in structural response prediction: A review, Afshar, A., Nouri, G., Ghazvineh, S., Lavassani, S. H. H., Practice Periodical on Structural Design and Construction, 29(3), 03124002, , 2024

95. An image is worth 16x16 words: Transformers for image recognition at scale, Dosovitskiy, A., arXiv preprint arXiv:2010.11929, , 2020

96. Artificial intelligence-based methods for renewable power system operation, Mei, S., Zeng, Z., C., 1(3), 163-179, , 2024

97. Ntire 2017 challenge on single image super-resolution: Methods and results, Timofte, R., Van Gool, L., Zhang, L., Agustsson, E., Yang, M. H., Proceedings of the IEEE conference on computer vision and pattern recognition workshops, , 2017

98. Steelex: A coupled expert system for integrated design of steel structures, Paek, Y., & Adeli, H., 1(3), 170-180, , 1988

99. Structural health monitoring using ai and ml based multimodal sensors data, Kumar, K. P., Pillai, V. J., Murthy, H., Chandra, S., Shibu, M., 27, 100762, , 2023

100. A new fuzzy strategy for size and topology optimization of truss structures, Mortazavi, A., 93, 106412, , 2020

101. Abngrad: Adaptive step size gradient descent for optimizing neural networks, Jiang, W., Liang, Y., Jiang, Z., Xu, D., & Zhou, L., 54(3), 2361-2378, , 2024

102. Deep learning in construction: Review of applications and potential avenues, Jacobsen, E. L., Teizer, J., 36(2), 03121001, , 2022

103. Toward human-level concept learning: Pattern benchmarking for ai algorithms, Holzinger, A., Saranti, A.,, Angerschmid, A., Finzel, B.,, Mueller, H., Schmid, U., &, 4(8), 100788, , 2023

104. Automated model-based 3d scan planning for prefabricated building components, Han, K., Son Rachel, H., 37(2), 04022058, , 2023

105. Faster RCNN: Towards RealTime Object Detection with Region Proposal Networks, He, K., Ren, S., Girshick, R., Sun, J., 39(6), 1137-1149, , 2017

106. Gradient descent algorithm to optimize the offshore scale squeeze treatments, Jordan, M., Mackay, E., Vazquez, O., Sorbie, K., Azari, V., 208, 109469, , 2022

107. Reinforcement learning for optimum design of a plane frame under static loads, Hayashi, K., Ohsaki, M., Harris, C., Alvey vision conference, Stephens, M., A combined corner and edge detector, Engineering with Computers, 37(3), 1999-2011, , 1988

108. Research on the application of gradient descent algorithm in machine learning, Wang, X., Zhang, Q., Yan, L., 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA), , 2021

109. The winter, the summer and the summer dream of artificial intelligence in law, Francesconi, E., 30(2), 147-161, , 2022

110. A lightweight encoder–decoder network for automatic pavement crack detection, Fan, Z., Wang, K. C. P., Zhu, G., Yuan, D., Ma, P., Liu, J., Sheng, W., Wang, M., Computer-Aided Civil and Infrastructure Engineering, 39(12), 1743-1765, , 2024

111. Comparison of shallow and deep neural networks for network intrusion detection, Kim, D. E., Gofman, M., 8-10 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), , 2018

112. Faster r-cnn: Towards real-time object detection with region proposal networks, Ren, S., He, K., Girshick, R., & Sun, J, 39(6), 1137-1149, , 2016

113. Reliability of non-destructive test approach on structural strength assessment, Bin Ayop, S. S., Lim, D. V., Recent Trends in Civil Engineering and Built Environment, 4(1), 098-107, , 2023

114. Vision based defect detection technologies in civil structures: A review study, Lv, S., Ma, Y., Chen, X., 53(2), 1456-1461, , 2024

115. Prediction of concrete compressive strength by combined non-destructive methods, Nobile, L., 50(2), 411-417, , 2015

116. Advances in computer vision-based civil infrastructure inspection and monitoring, Narazaki, Y., Hoskere, V., Spencer, B. F., Engineering, 5(2), 199-222, , 2019

117. Autonomous 3d vision-based bolt loosening assessment using micro aerial vehicles, Yang, T. Y., Tavasoli, S., Pan, X., Computer-Aided Civil and Infrastructure Engineering, 38(17), 2443- 2454, , 2023

118. Expert system methodologies and applications—a decade review from 1995 to 2004, Shu-Hsien, L., Expert Systems with Applications, 28(1), 93-103, , 2005

119. Feature engineering of machine-learning chemisorption models for catalyst design, Ma, X., Li, Z., Xin, H., 280, 232-238, , 2017

120. Simple and effective strategies to generate diverse designs for truss structures, Cai, Q., Ma, J., He, L., Feng, R., Xie, Y., 32, 268-278, , 2021

121. Mapping top-two-floor corner coordinates to building strains in deep latent space, Park, J. S., Jang, S. K., Hong, T., Lee, D.-E., & Park, H. S., 82, 108279, , 2024

122. Optimization algorithms: Ai techniques for design, planning, and control problems, Khamis, A., Simon and Schuster, , 2024

123. A review of image processing and quantification analysis for solid oxide fuel cell, Chen, P., Ooi, H. S., Lim, Z. H., Zhang, S., Tan, W. C., Xu, W., Tan, K. S.,, Tan, Y., Lam, C. K.,, 16, 100354 Building defect inspection and data management using computer vision, augmented reality, and bim technology. Automation in Construction, 160, 105318., , 2024

124. Combining old school autoencoder with cotracker for improved skin feature tracking, Nordling, T. E. M., Shi, W. P., 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), , 2024

125. Dr-avit: Toward diverse and realistic aerial visible-to-infrared image translation, Han, Z., Mei, S., Zhang, S., Su, Y., Chen, X., 62, 1-13, , 2024

126. Industrial expert systems review: A comprehensive analysis of typical applications, Zhu, C., Yang, X., IEEE Access, 12, 88558-88584, , 2024

127. Comparison of camera-based and lidar-based object detection for agricultural robots, Sari, S., Proceedings of International Conference on Information Technology and Applications. Singapore, , 2022

128. Computer vision and deep learning in insects for food and feed production: A review, Gebreyesus, G., Katumba, A., 216, 108503, , 2024

129. Freedom, ai and god: Why being dominated by a friendly super-ai might not be so bad, Luck, M., AI & SOCIETY, , 2024

130. Monocular camera based computer vision system for cost effective autonomous vehicle, Pidurkar, A., Sadakale, R., Prakash, A., 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), , 2019

131. A review of 3d reconstruction techniques in civil engineering and their applications, Liu, S., Ma, Z., 37, 163-174, , 2018

132. An approach to calculate depth of an object in a 2-d image and map it into 3-d space, Pradhan, A., Singh, S., Singh, A. K., 119(15), 27- 32, , 2015

133. Dual attention deep learning network for automatic steel surface defect segmentation, Pan, Y., Zhang, L., Computer-Aided Civil and Infrastructure Engineering, 37(11), 1468-1487, , 2022

134. Improvement of system reliability in a natural gas processing facility by pso and de, Saheb, T., Mellal, M. A., International Journal on Interactive Design and Manufacturing (IJIDeM), 18(1), 167-176, , 2024

135. Photo-realistic single image super-resolution using a generative adversarial network, Theis, L., Ledig, C., Huszár, F., Caballero, J., Acosta, A., Wang, Z., Aitken, A., Tejani, A., Cunningham, A., Totz, J., Proceedings of the IEEE conference on computer vision and pattern recognition, , 2017

136. Real-time tricolor phase measuring profilometry based on ccd sensitivity calibration, Zhu, L., Cao, Y., He, D., & Chen, C., 64(4), 379-387, , 2017

137. Rnns and lstm Handbook of medical image computing and computer assisted intervention, Dipietro, R., K. Zhou, D. Rueckert, S., G. FichtingerEds, Hager, G. D. In, Chapter 21 - deep learningpp. 503-519). Academic Press. https://doi. org/https://doi. org/10.1016/B978-0-12- 816176-0.00026-0, , 2020

138. Jumping nlp curves: A review of natural language processing research [review article], Cambria, E., & White, B, 9(2), 48-57, , 2014

139. Real-time 3-d shape measurement with composite phase-shifting fringes and multi-view system Multilayer perceptron (mlp). In M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, & F. Escobar (Eds.), Geomatic approaches for modeling land change scenarios (pp. 451-455). Springer International Publishing. https://doi. org/10.1007/978-3-319-60801-3_27, Mas, J. F., Zuo, C., Hu, Y., Feng, S., Taud, H., Da, J., Chen, Q., Tao, T., 24(18), 20253-20269, , 2016

140. Cross-section dimension measurement of construction steel pipe based on machine vision, Qin, Z., Yu, F., Li, R., Ji, Z., Mathematics 10(19), 3535, , 2022

141. Research on comparative of multi-surrogate models to optimize complex truss structures, Qin, Y., Yang, J., Yang, C., 28(6), 2268-2278, , 2024

142. A comparison of algorithms for inference and learning in probabilistic graphical models, Jojic, N., Frey, B. J., 27(9), 1392- 1416, , 2005

143. A novel deep unsupervised learning-based framework for optimization of truss structures, Lieu, Q. X., Lee, J, Kang, J., Mai, H. T., 39(4), 2585-2608, , 2023

144. Computer vision applications in construction: Current state, opportunities & challenges, Paneru, S., Jeelani, I., Automation in Construction, 132, 103940, , 2021

145. Estimation of the actual in-place concrete strength in assessing existing rc structures, Ferrini, M., Masi, A., Dolce, M., Proc. of the Second International fib Congress, , 2006

146. Surface defect detection of civil structures using images: Review from data perspective, Deng, L., Liu, P., Xiao, B., Wang, Q., Guo, J., Automation in Construction, 158, 105186, , 2024

147. A variable gradient descent shape optimization method for guide tee resistance reduction, Jing, R., Song, B., Gao, R., Yang, C., & Hao, X, 95, 110161, , 2024

148. Natural language processing for smart construction: Current status and future directions, Wang, J., Wu, C., Ren, Z., Li, X., Guo, Y., Yang, Z., Wang, M., Automation in Construction, 134, 104059, , 2022

149. Analysis of slam-based lidar data quality metrics for geotechnical underground monitoring, Fahle, L., Walton, G., Petruska, A. J., Brune, J. F., Holley, E. A., 39(5), 1939-1960, , 2022

150. Automl: A systematic review on automated machine learning with neural architecture search, Saha, P., Tuni, A., Salehin, I., Baten, M. A., Noman, S. M., Islam, M. S., Hasan, M. M., 2(1), 52-81, , 2024

151. Combined use of non-destructive tests for assessment of strength of concrete in structure, Jain, A., Verma, Y., Kumar, A., Kathuria, A., Murari, K., Procedia Engineering, 54, 241-251, , 2013

152. A review of computer vision–based structural health monitoring at local and global levels, Dong, C.-Z., Catbas, F. N., 20(2), 692-743, , 2020

153. Civil infrastructure defect assessment using pixel-wise segmentation based on deep learning, Savino, P., Tondolo, F., 13(1), 35- 48, , 2023

154. Machine learning with shallow neural networks Neural networks and deep learning: A textbook, Aggarwal, C. C., In C. C. Aggarwal (Ed, pp. 53-104 Springer International Publishing. https://doi. org/10.1007/978-3-319-94463-0_2, , 2018

155. Predicting concrete compressive strength using ultrasonic pulse velocity and rebound number, Huang, Q., Gardoni, P., Hurlebaus, S., 108(4), , 2011

156. Sensor-free stress estimation model for steel beam structures using a motion capture system, Park, H. S., Choi, S. W., Park, J. S., Oh, B. K., 16(8), 2701-2713, , 2016

157. On solving constrained optimization problems with neural networks: A penalty method approach, Lillo, W. E., Loh, M. H., Hui, S., & Zak, S. H., 4(6), 931-940, , 1993

158. Sizing, layout and topology design optimization of truss structures using the jaya algorithm, Lamberti, L., Degertekin, S. O., Ugur, I. B., 70, 903-928, , 2018

159. Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications, Zhao, W., Wang, L., Zhang, Z., Fan, H., Zhang, J., Mirjalili, S., Khodadadi, N., & Cao, Q, 238, 122200, , 2024

160. Recommendation for in situ concrete strength determination by combined nondestructive methods, Tc, R., 26(155), 92-98, , 1993

161. Benchmarking and improving dimensional quality on modular construction projects–a case study, Edwards, C., Rausch, C., Haas, C., 1(1), 2-21, , 2020

162. Improved multi-objective structural optimization with adaptive repairbased constraint handling, Cai, Y., Jelovica, J., 56(1), 118-137, , 2024

163. On the complexity of finding first-order critical points in constrained nonlinear optimization, Cartis, C., Gould, N. I. M., & Toint, P. L., 144(1), 93-106, , 2014

164. Automatic quality inspection of rebar spacing using vision-based deep learning with rgbd camera, Deng, L., Liu, M., Cao, R., Wang, S., Guo, J., ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, , 2024

165. Foundations & trends in multimodal machine learning: Principles, challenges, and open questions, Zadeh, A., Liang, P. P., Morency, L.-P., 56(10), Article 264, , 2024

166. Neural natural language processing for long texts: A survey on classification and summarization, Gkionis, I., Papadopoulos, G. T., Mademlis, I, Tsirmpas, D., 133, 108231, , 2024

167. Unpacking reward shaping: Understanding the benefits of reward engineering on sample complexity, Gupta, A., Zhai, Y., Pacchiano, A., Kakade, S., Levine, S., Advances in Neural Information Processing Systems, 35, 15281-15295, , 2022

168. Constraint-aware optimization model for plane truss structures via single-agent gradient descent, Park, J. S., Hong, T., Lee, D.-E., & Park, H. S., 39(18), 2737-2759, , 2024

169. From darkness to clarity: A comprehensive review of contemporary image shadow removal research (, Chuah, J. H., Chow, C.-O., Zhu, X., 148, 105100, , 2024

170. The strategic use of ai in the public sector: A public values analysis of national ai strategies, Sigurdarson, H. T., Hjaltalin, I. T., 41(1), 101914, , 2024

171. A multi-agent optimization algorithm and its application to training multilayer perceptron models, Chauhan, D., Neri, F., Yadav, A., 15(3), 849-879, , 2024

172. Automated estimation of reinforced precast concrete rebar positions using colored laser scan data, Cheng, J. C., Sohn, H., Wang, Q., Computer‐Aided Civil and Infrastructure Engineering, 32(9), 787-802, , 2017

173. Machine learning and computer vision based methods for cancer classification: A systematic review, Patil, H. Y., Mukadam, S. B., 31(5), 3015-3050, , 2024

174. Multi-objective seismic design optimization of steel frames by a chaotic meta-heuristic algorithm, Baghchevan, A., Gholizadeh, S., 33, 1045-1060, , 2017

175. Multimodal size, shape, and topology optimisation of truss structures using the firefly algorithm, Miguel, L. F. F., Lopez, R. H., Miguel, L. F. F., Advances in Engineering Software, 56, 23-37, , 2013

176. Bayesian dynamic forecasting of structural strain response using structural health monitoring data, Wang, Y. W., Ni, Y. Q, 27(8), e2575, , 2020

177. High-precision dimensional measurement of a curtain wall crosssection using image super-resolution, Park, H. S., Park, J. S., 9th Asia-Pacific Workshop on Structural Health Monitoring, 9APWSHM 2022, , 2023

178. Knowledge-based expert systems in structural design Advances and trends in structures and dynamics, In A. K. Noor R. J. Hayduk (Eds, Maher, M. L., Fenves, S. J, Sriram, D., pp. 1-9 Pergamon. https://doi. org/https://doi. org/10.1016/B978-0-08-032789-1.50004-5, , 1985

179. Monitoring structural responses during proof load testing of reinforced concrete bridges: A review, Naaktgeboren, M., Van Der Veen, C., Lantsoght, E., Yang, Y., Garnica, G. Z., Fennis, S., Zhang, F., Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, 2339-2346, , 2021

180. Autonomous dimensional inspection and issue tracking of rebar using semantically enriched 3d models, Chen, Y.-H., Chang, C.-C., Lin, J. J., Chen, C.-S., Huang, T.-W., Automation in Construction, 160, 105303, , 2024

181. Object recognition from local scale-invariant features Artificial intelligence in civil engineering, Lowe, D. G. 20-27, Lu, P., Chen, S., & Zheng, Y, Sept. 2012(1), 145974, , 1999

182. Rule-based natural language processing for automation of stroke data extraction: A validation study, Mamdani, M., Pou-Prom, C., Aviv, R. I., Miguel, O., Liu, Z. A., Gunter, D., Puac-Polanco, P., Yu, A. Y. X., Thornhill, R. E., 64, , 2022

183. A systematic review of convolutional neural network-based structural condition assessment techniques, Sadhu A, Dunphy K, Capretz M., Sony S, Engineering Structures, 226, 111347, , 2021

184. An efficient lightgbm-based differential evolution method for nonlinear inelastic truss optimization, Truong, V.-H., Tangaramvong, S., & Papazafeiropoulos, G., 237, 121530, , 2024

185. A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge, Brownjohn, J., Kong, D., Xu, Y., Structural Control and Health Monitoring, 25(5), e2155, , 2018

186. Monitoring of long-term prestress losses in prestressed concrete structures using fiber optic sensors, Glisic, B., Abdel-Jaber, H., 18(1), 254-269, , 2018

187. Review on computer vision-based crack detection and quantification methodologies for civil structures, Deng, J., Lu, Y., Lee, V. C.-S., Zhou, Y., Singh, A., 356, 129238, , 2022

188. Weld cross-section profile fitting and geometric dimension measurement method based on machine vision, Zhang, A., He, W., Wang, P., 13(7), 4455, , 2023

189. Structural health monitoring of civil engineering structures by using the internet of things: A review, Mishra, M., Ramana, G. V., Lourenço, P. B., 48, 103954, , 2022

190. Usages of metaheuristic algorithms in investigating civil infrastructure optimization models; a review, Ghannadiasl, A., Ghaemifard, S., AI in Civil Engineering, 3(1), 17, , 2024

191. A comparative investigation using machine learning methods for concrete compressive strength estimation, Göymen, S., Güçlüer, K., Özbeyaz, A., Günaydın, O., 27, 102278, , 2021

192. Adaptive approximation for multiple sensor fault detection and isolation of nonlinear uncertain systems, Panayiotou, C. G., Reppa, V., Polycarpou, M. M., 25(1), 137-153, , 2014

193. End-toend point cloud-based segmentation of building members for automating dimensional quality control, Arashpour, M., Masoumi, H., Mirzaei, K., Asadi, E., Gonzalez, V., Mahdiyar, A., Advanced Engineering Informatics, 55, 101878, , 2023

194. Machine learning based evaluation of concrete strength from saturated to dry by non-destructive methods, Güçlüer, K., Günaydın, O., Özbeyaz, A., Akbaş, E., 76, 107174, , 2023

195. A survey on automatic generation of figurative language: From rulebased systems to large language models, Lai, H., Nissim, M., 56(10), Article 244, , 2024

196. An online reinforcement learning-based energy management strategy for microgrids with centralized control, Meng, Q., Hussain, S., Luo, F., Jin, X., Wang, Z., IEEE Transactions on Industry Applications, 1-10, , 2024

197. Concrete compressive strength prediction using non-destructive tests through response surface methodology, Ghasemi, M., Azhdary Moghaddam, M., Poorarbabi, A., 11(4), 939-949, , 2020

198. Experimental studies on behavior of hybrid materials concrete using non-destructive testing (ndt) methods, Ravekar, V., Gautham Kishore Reddy, G., Rama Rao, P., Kulkarni, P., Kanhe, N., Materials Today: Proceedings, , 2023

199. Non-destructive strength evaluation of concrete: Analysis of some key factors using synthetic simulations, Breysse, D., Sbartaï, Z. M., Alwash, M., Construction and Building Materials, 99, 235-245, , 2015

200. Reconstruction of lost depth data in multiview video-plus-depth communications using geometric transforms, Marcelino, S., De Faria, S. M. M., Assuncao, P., Soares, S., 40, 589-599, , 2016

201. Soft computing techniques for the prediction of concrete compressive strength using non-destructive tests, Asteris, P. G., Lourenço, P. B., Bardhan, A., Skentou, A. D., Samui, P., 303, 124450, , 2021

202. Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection, Chang, C.-T., Lin, T.-H., Putranto, A., 133, 108618, , 2024

203. A quantitative comparison between size, shape, topology and simultaneous optimization for truss structures, Klashorst, E. V. D., Müller, T. E., 14, , 2017

204. Automated reconstruction model of a cross-sectional drawing from stereo photographs based on deep learning, Park, H. S., Park, J. S., Computer-Aided Civil and Infrastructure Engineering, 39(3), 383-405, , 2024

205. Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms, Akbari, M., Rezazadeh Eidgahee, D., Fakharian, P., Jahangir, H., Ali Taeb, A., 47, 1790-1802, , 2023

206. Criteria for identifying concrete homogeneous areas for the estimation of in-situ strength in rc buildings, Manfredi, V., Masi, A., Chiauzzi, L., 121, 576-587, , 2016

207. Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry, Cheng, M. H., Mantha, B. R. K., Ham, Y., Lin, J. J., Liang, C.-J., Le, T.-H., 162, 105369, , 2024

208. Evolving deep neural networks In Artificial intelligence in the age of neural networks and brain computing, Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., & Duffy, N., pp. 269-287, , 2024

209. A novel methodology for anomaly detection in smart home networks via fractional stochastic gradient descent, Bajpai, A., Chaurasia, D., & Tiwari, N., 119, 109604, , 2024

210. Computer vision, pattern recognition and image processing in left ventricle segmentation: The last 50 years, Suri, J. S., 3(3), 209-242, , 2000

211. Computer vision: Models, learning, and inference Smart rebar progress monitoring using 3d point cloud model, Saad, S., Prince, S. J., Murtiyoso, A., Alaloul, W. S., Qureshi, A. H., Musarat, M. A., Hussain, S. J., Cambridge University Press Expert Systems with Applications, 249, 123562., , 2012

212. Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and bim, Sohn, H., Cheng, J. C. P., Wang, Q., Chang, C.-C., Park, J.-W., Kim, M.-K., Automation in Construction, 72, 102-114, , 2016

213. Construction quality control of concrete structures in architectural engineering—a case in shanghai, china, Deng, F., Soltani, A., Mehdipour, A., 2(3), 256-271, , 2024

214. Fast topology optimization of phononic crystal-based metastructures for vibration isolation by deep learning, Liu, C.-X., Yu, G.-L., Liu, Z., Computer-Aided Civil and Infrastructure Engineering, 39(5), 776-790, , 2024

215. 2024// Natural language processing for infrastructure resilience to natural disasters: A scientometric review, Moriyani, M. A., Asaye, L., Le, C., Le, T., & Le, T., 4-5 April, , 2024

216. Artificial intelligence-based position control: Reinforcement learning approach in spring mass damper systems, Demircioğlu, U., Bakır, H., 99(4), 046003, , 2024

217. Compressive strength evaluation by non-destructive techniques: An automated approach in construction industry, Waqas, R., Rashid, K., 12, 147-154, , 2017

218. Optimization of reinforced concrete columns according to different environmental impact assessment parameters, Kripka, M., De Medeiros, G. F., Engineering Structures, 59, 185-194, , 2014

219. Metaheuristic optimization algorithms: A comprehensive overview and classification of benchmark test functions, Sharma, P., Raju, S., 28(4), 3123-3186, , 2024

220. Enhancement of sparse 3d reconstruction using a modified match propagation based on particle swarm optimization, Satori, K., Merras, M., El Akkad, N., El Hazzat, S., Saaidi, A., 78(11), 14251-14276, , 2019

221. Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics, Patel, V. K., Savsani, P. V., Savsani, V. J., Tejani, G. G., 5(2), 198-214, , 2017

222. Urban safety network for long-term structural health monitoring of buildings using convolutional neural network, Oh, B. K., Park, H. S., Automation in Construction, 137, 104225, , 2022

223. Fluid antennas-enabled multiuser uplink: A low-complexity gradient descent for total transmit power minimization, Hu, G., Wu, Q., Xu, K., Ouyang, J., Si, J., Cai, Y., & Al-Dhahir, N., 28(3), 602-606, , 2024

224. Artificial intelligence quality inspection of steel bars installation by integrating mask r-cnn and stereo vision, Kardovskyi, Y., Moon, S., Automation in Construction, 130, 103850, , 2021

225. Automatic measurement of rebar spacing based on 3d point cloud segmentation using rebaryolov8- seg and depth data, Liao, T., Song, W., Lu, T., Jiang, Z., Wang, J., Song, J., Zhu, Q., Yang, L., Zhou, H., 116111, , 2024

226. Comparative relationships of direct, indirect, and semi-direct ultrasonic pulse velocity measurements in concrete, Kucuk, O. F., Turgut, P., 42(11), 745-751, , 2006

227. Multi-objective optimization of truss structure using multi-agent reinforcement learning and graph representation, Ohsaki, M., Hayashi, K., Kupwiwat, C.-T., 129, 107594, , 2024

228. A multi-stage descent algorithm for discrete and continuous optimization applied to truss structures optimal design, Sellami, M., 234(10), 4837-4857, , 2023

229. Attention is need Advances Benchmarking adversarial robustness of image shadow removal with shadow-adaptive attacks, Guo, L., Vaswani, A. all you Information Processing Systems, Wang, C., Yu, Y., Wen, B, in Neural2024 14-19 April 2024 ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., , 2017

230. Computer vision for shm of civil infrastructure: From dynamic response measurement to damage detection – a review, Feng, D., Feng, M. Q., Engineering Structures, 156, 105-117, , 2018

231. Real-time assessment of rebar intervals using a computer vision-based dvnet model for improved structural integrity, K. R, S. P., Kim, B., V, D., An, J., Natarajan, Y., Lee, D.-E., 21, e03707, , 2024

232. Revolutionizing animation: Unleashing the power of artificial intelligence for cutting-edge visual effects in films, Reddy, V. S., Kathiravan, M., & Reddy, V. L., 28(1), 749-763, , 2024

233. Role of expert systems to optimize the friction stir welding process parameters using numerical modelling: A review, Sharma, Y., Vasudev, H., Mehta, A., Singh, H., 18(5), 2609-2625, , 2024

234. Continuous-time gradient-like descent algorithm for constrained convex unknown functions: Penalty method application, Solis, C. U., Clempner, J. B., & Poznyak, A. S., 355, 268-282, , 2019

235. Automated building damage assessment and large-scale mapping by integrating satellite imagery, gis, and deep learning, Braik, A. M., Koliou, M., Computer-Aided Civil and Infrastructure Engineering, 39(15), 2389-2404, , 2024

236. Prove non distruttive sulle costruzioni in cemento armato Applications of natural language processing in construction, Ma, J., Luo, X., Pascale, G., Di Leo, A., Ding, Y., 136, 104169, , 1994

237. A computer vision-based approach to automatically extracting the aligning information of precast structural components, Guo, H., Luo, Z., Zhou, Y., Ye, X., Automation in Construction, 164, 105478, , 2024

238. Advancements and challenges in the application of artificial intelligence in civil engineering: A comprehensive review, Harle, S. M., 25(1), 1061-1078, , 2024

239. Automatic evaluation of rebar spacing and quality using lidar data: Field application for bridge structural assessment, Sarlo, R., Lippitt, C. D., Zhang, S., Alampalli, S., Hojati, M., Smith, A., Moreu, F., Yuan, X., Automation in Construction, 146, 104708, , 2023

240. Model-reference health monitoring of hysteretic building structure using acceleration measurement with test validation, Shan, J., Shi, W., Lu, X., Computer-Aided Civil and Infrastructure Engineering, 31(6), 449-464, , 2016

241. Transformers:The end of history for natural language processing? Machine Learning and Knowledge Discovery in Databases, Chernyavskiy, A., Ilvovsky, D., Nakov, P., Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain,, 2021, Proceedings, Part III 21, , 2021

242. Optimizing earthquake design of reinforced concrete bridge infrastructures based on evolutionary computation techniques, Horta, N., Oliveira, C. S., Camacho, V. T., Lopes, M., 61(3), 1087-1105, , 2020

243. An efficient data-driven framework for detecting infeasible solutions in multiobjective evolutionary bilevel optimization, Mejía-De-Dios, J. A., Rodríguez-Molina, A., & Mezura-Montes, E., 1-1, , 2024

244. Characteristic concrete compressive strength of existing structures— evaluation of en 13791:2019 for small sample sizes, Glock, C., Sefrin, R., Structural Concrete, 23(2), 822-835, , 2022

245. Constructability-based design approach for steel structures: From truss beams to real-world inspired industrial buildings, Aloisio, A., Rad, M. M., Domaneschi, M., Cucuzza, R., Automation in Construction, 166, 105630, , 2024

246. Inverse problem based multiobjective sunflower optimization for structural health monitoring of three-dimensional trusses, Magacho, E. G., Jorge, A. B., & Gomes, G. F., 16(1), 247-267, , 2023

247. A soul emerges when ai, ar, and anime converge: A case study on users of the new anime-stylized hologram social robot hupo, Wu-Ouyang, B., Leo-Liu, J., 26(7), 3810-3832, , 2022

248. Advancing concrete strength prediction using nondestructive testing: Development and verification of a generalizable model, Jalalpour, M., Delatte, N., Amini, K., Construction and Building Materials, 102, 762-768, , 2016

249. Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains, Brown, N. K., Li, G., Fadel, G. M., Garland, A. P., 218, 110672, , 2022

250. Long short-term memory Holl, C.2024 The content intelligence: An argument against the lethality of artificial intelligence, Hochreiter, S., 4(1), 13, , 1997

251. Ndt testing methods for estimating compressive strength in finished structures–evaluation of accuracy and testing system, Bellander, U., RILEM Symp. Proc. on Quality Control of Concrete Structures, Session, , 1979

252. A cascaded lidar-camera fusion network for road detection Intelligent design of shear wall layout based on diffusion models, Gu, Y., Lu, X, Liao, W., Kong, H., Yang, J., Huang, Y., Gu, S., 2021 IEEE International Conference on Robotics and Automation (ICRA Computer-Aided Civil and Infrastructure Engineering, n/a(n/a)., , 2021

253. Assessing the strength of reinforced concrete structures through ultrasonic pulse velocity and schmidt rebound hammer tests, Shariati, M., Ramli-Sulong, N. H., Shafigh, P., Arabnejad, M. M., Sinaei, H., 6(1), 213-220, , 2011

254. Automated dimensional quality assessment for formwork and rebar of reinforced concrete components using 3d point cloud data, Wang, Q., Thedja, J. P. P., Kim, M.-K., Automation in Construction, 112, 103077, , 2020

255. A novel natural language processing (nlp)–based machine translation model for english to pakistan sign language translation, Khan, N. S., Abid, A., Abid, K., 12(4), 748-765, , 2020

256. Study on the introduction into italy on the combined nondestructive method, for the determination ofin situ concrete strength, Cianfrone, F., & Facaoaru, I, 12(5), 413-424, , 1979

257. Estimation of concrete compressive strength from non-destructive tests using a customized neural network and genetic algorithm, Lee, D.-E., Park, S., Hong, T., Park, H. S., Oh, B. K., Park, J. S., 164, 111941, , 2024

258. Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression techniques, Erdal, M., 4(10), 1057- 1065, , 2009

259. Correlation between compressive strength of concrete and ultrasonic pulse velocity: A case of study and a new correlation method, Nuñez, E., Mata, R., Ruiz, R. O., 369, 130569, , 2023

260. Handbook Minimizing carbon emission of prefabricated reinforced concrete t-beams using bim and two-stage metaheuristic searching, Chen, C. H. pattern recognition and computer vision World scientific., Chen, K., Zhang, Y. You, B. Li, M., 38(1), 04023041, , 2015

261. Optimal sensor placement for bridge structural health monitoring: Integration of physics-based models with datadriven approaches, Masciotta, M. G., Barontini, A., Pellegrini, D., Brando, G., & Lourenço, P. B, 62, 932-939, , 2024

262. Practical wireless safety monitoring system of long-span girders subjected to construction loading a building under construction, Kim, Y., Kim, J. M., Oh, B. K., Kim, S. H., Cho, T., Park, J. S., Park, H. S., Measurement, 146, 524-536, , 2019

263. Structural optimization for multiple structure cases and multiple payload cases with a two-level multipoint approximation method, An, H., Huang, H., Chen, S., 29(5), 1273-1284, , 2016

264. The use of combined non destructive testing in the concrete strength assessment from laboratory specimens and existing buildings, Vishal, T., Vijay, G., Shweta, P., Nikhil, M., Deep, C., Minal, B., 2, 55-59, , 2015

265. Combination of three non-destructive methods for the determination of the strength of concrete Decoupled weight decay regularization, Logothetis, L. Loshchilov, I, arXiv preprint arXiv:1711.05101, , 1979

266. A new simple, fast and efficient algorithm for global optimization over continuous search-space problems: Radial movement optimization, Rahmani, R., & Yusof, R., 248, 287-300, , 2014

267. Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: A review, Azad, M. M., Cheon, Y. B., Kim, S., Kim, H. S., Advanced Composite Materials 33(2), 162-188, , 2024

268. Semi-autonomous inspection for concrete structures using digital models and a hybrid approach based on deep learning and photogrammetry, Sas, G., Popescu, C., Täljsten, B., Gonzalez-Libreros, J., Mirzazade, A., Blanksvärd, T., 13(8), 1633-1652, , 2023

269. Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements, Glisic, B., Oh, B. K., Park, H. S., Automation in Construction, 126, 103665, , 2021

270. Vision-based inspection of prefabricated components using camera poses: Addressing inherent limitations of image-based 3d reconstruction, Nie, G.-Y., Lee, D., Han, K., 64, 105710, , 2023

271. Efficient constraint handling based on the adaptive penalty method with balancing the objective function value and the constraint violation, Kawachi, T., Hara, A., Takahama, T., Kushida, J. I., 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA), , 2019

272. A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications, Inman, D. J., Kiranyaz, S., Avci, O., Hussein, M., Abdeljaber, O., Gabbouj, M., Mechanical Systems and Signal Processing, 147, 107077, , 2021

273. Machine learning techniques for diagrid building design: Architectural–structural correlations with feature selection and data augmentation, Entezami, A., Ghisi, A., Kazemi, P., 86, 108766, , 2024

274. Unpacking transformer-based nlp Understanding generative ai business applications: A guide to technical principles and real-world applications, In I. Cronin (Ed., Cronin, I, pp. 75-86 Apress. https://doi. org/10.1007/979-8-8688-0282-9_5, , 2024

275. Adam: A method for stochastic optimization The use of combined non-destructive testing methods to determine the compressive strength of concrete, Knaze, P. & Beno, P., Kingma, D. P., 17(3), 207-210, , 2014

276. Concrete compressive strength prediction using neural networks based on non-destructive tests and a self-calibrated response surface methodology, Azhdary Moghaddam, M., Poorarbabi, A., Ghasemi, M., 39(4), 78, , 2020

277. Simplifying radiologic reports with natural language processing: A novel approach using chatgpt in enhancing patient understanding of mri results, Navas, L., Cucos, T., Feucht, M., Zimmerer, A., Schmidt, S., 144(2), 611-618, , 2024

278. Design model for analysis of relationships among co2 emissions, cost, and structural parameters in green building construction with composite columns, Choi, S. W., Park, J. S., Oh, B. K., Park, H. S., 118, 301-315, , 2016

279. Image-based 3d reconstruction for multiscale civil and infrastructure projects: A review from 2012 to 2022 with new perspective from deep learning methods, Fan, S., Wang, S., Li, P., Lu, Y., Tang, P., Lu, J., Advanced Engineering Informatics, 59, 102268, , 2024

280. Analysis of the single and combined non-destructive test approaches for on-site concrete strength assessment: General statements based on a real case-study, Kenai, S., Breysse, D., Ali-Benyahia, K., Sbartaï, Z.-M., Ghrici, M., 6, 109-119, , 2017

281. An exact penalty function optimization method and its application in stress constrained topology optimization and scenario based reliability design problems, Yuan, X., Liao, H., Gao, R., 125, 260-292, , 2024

282. Artificial intelligent in optimization of steel moment frame structures review Perspective correction of building facade images for architectural applications, Soori, M., Jough, F. K. G., Soycan, M., Soycan, A., A International Journal of Structural and Construction Engineering Engineering Science and Technology, an International Journal, 22(3), 697-705, , 2024

283. Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features, Nežerka, V., Zbíral, T., Trejbal, J., Expert Systems with Applications, 238, 121568, , 2024

284. Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems Deep reinforcement learning for process design: Review and perspective, Gao, Q., & Schweidtmann, A. M., Gandomi, A. H., Yang, X.-S., & Alavi, A. H., 29(1), 17-35, , 2013

285. Analysis of the accuracy of in-situ concrete characteristic compressive strength assessment in real structures using destructive and non-destructive testing methods, Sbartaï, Z.-M., Kenai, S., Ghrici, M., Ali-Benyahia, K., Elachachi, S.-M., 366, 130161, , 2023

286. Nature-inspired metaheuristic algorithms for constraint handling: Challenges, issues, and research perspective Constraint handling in metaheuristics and applications, Kaul, S., Kumar, Y, In A. J. Kulkarni, E. Mezura-Montes, Y. Wang, A. H. Gandomi, & G. Krishnasamy (Eds, pp. 55-80, , 2021

287. A wireless vibrating wire sensor node for continuous structural health monitoring Heuristic search for new microcircuit structures: An application of artificial intelligence. AI Magazine, 3(3), 17., Lenat, D. B., Sho, K., Lee, H. M., Sutherland, W. R., Park, H. S., Kim, J. M., Gibbons, J., 19(5), 055004, , 2010

288. The role of artificial intelligence in generating original scientific research Prediction of concrete compressive strength using non-destructive test results. Computers and Concrete, 21(4), 407-417., Gaisford, S., Erdal, H. I, Basit, A. W.,, Erdal, H. Erdal, M., Simsek, O., Elbadawi, M., Li, H.,, 652, 123741, , 2024

289. Deep reinforcement learning for automated design of reinforced concrete structures 3d reconstruction of spherical images: A review of techniques, applications, and prospects. Geo-spatial Information Science, 1-30., Jo, H., Jeong, J.-H., Weng, D., Jiang, S., Chen, W., You, K., Li, Y., Computer-Aided Civil and Infrastructure Engineering, 36(12), 1508-1529, , 2021