BeeLab
Research Field
Minh-Tu Cao is an accomplished academic and researcher in the field of Construction Engineering and Management. With a Bachelor's degree from the National Civil Engineering University in Vietnam, followed by a Master's and Ph.D. from the National Taiwan University of Science and Technology, Minh-Tu's educational journey laid the foundation for an impressive career. Before becoming an Assistant Professor in the Department of Civil Engineering at National Yang Ming Chiao Tung University, Minh-Tu worked in the construction industry for several years. This hands-on experience provided invaluable insights into the real-world challenges and intricacies of the field.
Currently, Minh-Tu's work revolves around the application of Artificial Intelligence in the realm of Construction Engineering and Management, as well as the strategic use of Building Information Modeling (BIM) in construction management processes. This includes exploring areas such as Computational Intelligence, Optimization in Construction, and the integration of BIM technologies to enhance project efficiency and quality.
Minh-Tu's passion for research has borne fruit in the form of many publications in high-ranking SCI journals. Additionally, Minh-Tu has received several awards for best and outstanding papers in international conferences, highlighting the impact of their work. Minh-Tu Cao's dedication to advancing the fields of Construction Engineering and Artificial Intelligence, along with their expertise in leveraging BIM for improved construction management, is both inspiring and groundbreaking, promising a future of innovation and excellence in the industry.
BeeLab is dedicated to advancing research at the intersection of Construction Engineering, Artificial Intelligence (AI), and Building Information Modeling (BIM). The BeeLab primarily focuses on leveraging cutting-edge AI technologies to address critical challenges in construction management, with a particular emphasis on optimizing project efficiency, quality, and sustainability.
The BeeLab has produced numerous high-impact publications in leading SCI journals, contributing significantly to the body of knowledge in AI applications for construction engineering. We actively collaborate with industry partners and academic institutions to bridge the gap between theory and practice, ensuring that our research advances scientific understanding and delivers practical, real-world solutions.
The BeeLab is committed to pushing the boundaries of innovation in construction engineering and management, strongly emphasizing interdisciplinary approaches and state-of-the-art AI techniques. Through our research, we aim to make a lasting impact on the construction industry, driving efficiency, safety, and sustainability.
Join Us! We welcome highly motivated graduate students and talented researchers passionate about AI, construction engineering, and BIM technologies to join our lab. Whether you're looking to pursue a Master's or Ph.D. or seeking postdoctoral opportunities, our lab provides a dynamic and collaborative environment where you can contribute to cutting-edge research and gain valuable industry-relevant experience. Together, we can push the boundaries of innovation and make meaningful contributions to the future of the construction industry.
Our research spans a wide range of topics, including:
Artificial Intelligence in Construction Engineering: We explore integrating AI techniques such as deep learning, machine learning, and metaheuristic optimization to solve complex problems in construction engineering, including predicting mechanical behavior, optimizing resource allocation, and enhancing structural integrity.
Building Information Modeling (BIM) for Project Efficiency: We focus on utilizing BIM technologies to improve the management and coordination of construction projects, from design through execution. Our research also investigates how BIM can be combined with AI-driven decision-making to optimize energy consumption, reduce waste, and ensure sustainable construction practices.
Computational Intelligence and Optimization: Our lab delves into computational intelligence methods like evolutionary algorithms and machine learning ensembles to solve optimization problems in construction.
PUBLICATIONS IN sci journals
[1] M.-T. Cao, "Drone-assisted segmentation of tile peeling on building façades using a deep learning model," Journal of Building Engineering, vol. 80, p. 108063, 2023/12/01/ 2023, doi: https://doi.org/10.1016/j.jobe.2023.108063.
[2] T.-H. Nguyen, D.-H. Tran, N.-M. Nguyen, H.-T. Vuong, C. Chien-Cheng, and M.-T. Cao, "Accurately predicting the mechanical behavior of deteriorated reinforced concrete components using natural intelligence-integrated Machine learners," Construction and Building Materials, vol. 408, p. 133753, 2023/12/08/ 2023, doi: https://doi.org/10.1016/j.conbuildmat.2023.133753. (Corresponding author)
[3] M.-T. Cao, "Advanced soft computing techniques for predicting punching shear strength," Journal of Building Engineering, vol. 79, p. 107800, 2023/11/15/ 2023, doi: https://doi.org/10.1016/j.jobe.2023.107800.
[4] N.-M. Nguyen, W.-C. Wang, and M.-T. Cao*, "Early estimation of the long-term deflection of reinforced concrete beams using surrogate models," Construction and Building Materials, vol. 370, p. 130670, 2023/03/17/ 2023, doi: https://doi.org/10.1016/j.conbuildmat.2023.130670. (Corresponding author)
[5] M.-Y. Cheng, M.-T. Cao*, and N.-M. Dao-Thi, "A novel artificial intelligence-aided system to mine historical high-performance concrete data for optimizing mixture design," Expert Systems with Applications, vol. 212, p. 118605, 2023/02/01/ 2023, doi: https://doi.org/10.1016/j.eswa.2022.118605. (Corresponding author)
[6] H. Nguyen, M.-T. Cao, X.-L. Tran, T.-H. Tran, and N.-D. Hoang, "A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles," Neural Computing and Applications, 2022/10/15 2022, doi: 10.1007/s00521-022-07896-w.
[7] M.-T. Cao, N.-M. Nguyen, and W.-C. Wang, "Using an evolutionary heterogeneous ensemble of artificial neural network and multivariate adaptive regression splines to predict bearing capacity in axial piles," Engineering Structures, vol. 268, p. 114769, 2022/10/01/ 2022, doi: https://doi.org/10.1016/j.engstruct.2022.114769.
[8] M.-Y. Cheng, M.-T. Cao*, and C. K. Nuralim, "Computer vision-based deep learning for supervising excavator operations and measuring real-time earthwork productivity," The Journal of Supercomputing, 2022/09/27 2022, doi: 10.1007/s11227-022-04803-x. (Corresponding author)
[9] W.-C. Wang, N.-M. Nguyen, and M.-T. Cao*, "Smart ensemble machine learner with hyperparameter-free for predicting bond capacity of FRP-to-concrete interface: Multi-national data," Construction and Building Materials, vol. 345, p. 128158, 2022/08/22/ 2022, doi: https://doi.org/10.1016/j.conbuildmat.2022.128158. (Corresponding author)
[10] M.-Y. Cheng, M.-T. Cao*, and I. F. Huang, "Hybrid artificial intelligence-based inference models for accurately predicting dam body displacements: A case study of the Fei Tsui dam," Structural Health Monitoring, vol. 21, no. 4, pp. 1738-1756, 2022/07/01 2021, doi: 10.1177/14759217211044116. (Corresponding author)
[11] H. Nguyen, N.-M. Nguyen, M.-T. Cao, N.-D. Hoang, and X.-L. Tran, "Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine," Engineering with Computers, vol. 38, no. 2, pp. 1255-1267, 2022/06/01 2022, doi: 10.1007/s00366-020-01260-z.
[12] M.-T. Cao, K.-T. Chang, N.-M. Nguyen, V.-D. Tran, X.-L. Tran, and N.-D. Hoang, "Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach," Soft Computing, vol. 25, no. 20, pp. 12839-12855, 2021/10/01 2021, doi: 10.1007/s00500-021-06086-5.
[13] Cao, M.-T., N.-M. Nguyen, K.-T. Chang, X.-L. Tran and N.-D. Hoang (2021). "Automatic recognition of concrete spall using image processing and metaheuristic optimized LogitBoost classification tree." Advances in Engineering Software 159: 103031.
[14] M.-Y. Cheng, M.-T. Cao*, and A. Y. Jaya Mendrofa, "Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine," Journal of Building Engineering, vol. 35, p. 101973, 2021/03/01/ 2021, doi: https://doi.org/10.1016/j.jobe.2020.101973. (Corresponding author)
[15] D. T. Vu, X.-L. Tran, M.-T. Cao, T. C. Tran, and N.-D. Hoang, "Machine learning based soil erosion susceptibility prediction using social spider algorithm optimized multivariate adaptive regression spline," Measurement, vol. 164, p. 108066, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.measurement.2020.108066.
[16] H. Nguyen, N.-M. Nguyen, M.-T. Cao, X.-L. Tran, and N.-D. Hoang, "Prediction of Long-Term Deflections of Reinforced Concrete Members Using a Novel Swarm Optimized Extreme Gradient Boosting Machine," Engineering with Computers, vol. In Press, 12/22 2020.
[17] M.-Y. Cheng, M.-T. Cao*, and P.-K. Tsai, "Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: a Taiwan case study," Journal of Computational Design and Engineering, vol. 8, no. 1, pp. 268-282, 2020, doi: 10.1093/jcde/qwaa077. (Corresponding author)
[18] M.-Y. Cheng, M.-T. Cao*, and J. G. Herianto, "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, vol. 138, p. 109869, 2020/09/01/ 2020, doi: https://doi.org/10.1016/j.chaos.2020.109869. (Corresponding author)
[19] M.-T. Cao, Q.-V. Tran, N.-M. Nguyen, and K.-T. Chang, "Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources," Advanced Engineering Informatics, vol. 46, p. 101182, 2020/10/01/ 2020, doi: https://doi.org/10.1016/j.aei.2020.101182 .
[20] M.-T. Cao, N.-D. Hoang, V. H. Nhu, and D. T. Bui, "An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength," Engineering with Computers, 2020/11/02 2020, doi: 10.1007/s00366-020-01116-6.
[21] M.-Y. Cheng, J.-S. Chou, and M.-T. Cao*, "Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance," Soft Computing, vol. 21, no. 2, pp. 477-489, 2017/01/01 2017, doi: 10.1007/s00500-015-1798-y. (Corresponding author)
[22] D.-H. Tran, M.-Y. Cheng, and M.-T. Cao, "Solving Resource-Constrained Project Scheduling Problems Using Hybrid Artificial Bee Colony with Differential Evolution," Journal of Computing in Civil Engineering, vol. 30, no. 4, p. 04015065, 2016/07/01 2016, doi: 10.1061/(ASCE)CP.1943-5487.0000544.
[22] M.-Y. Cheng, D.-H. Tran, and M.-T. Cao, "Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time-cost-quality trade-off," Journal of Civil Engineering and Management, vol. 22, no. 2, pp. 210-223, 2016/02/17 2016, doi: 10.3846/13923730.2014.897972.
[23] M.-Y. Cheng and M.-T. Cao*, "Estimating strength of rubberized concrete using evolutionary multivariate adaptive regression splines," Journal of Civil Engineering and Management, vol. 22, no. 5, pp. 711-720, 2016/07/03 2016, doi: 10.3846/13923730.2014.897989. (Corresponding author)
[24] D.-H. Tran, M.-Y. Cheng, and M.-T. Cao, "Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem," Knowledge-Based Systems, vol. 74, pp. 176-186, 2015/01/01/ 2015, doi: https://doi.org/10.1016/j.knosys.2014.11.018.
[25] M.-Y. Cheng, M.-T. Cao*, and Y.-W. Wu, "Predicting Equilibrium Scour Depth at Bridge Piers Using Evolutionary Radial Basis Function Neural Network," Journal of Computing in Civil Engineering, vol. 29, no. 5, p. 04014070, 2015/09/01 2015, doi: 10.1061/(ASCE)CP.1943-5487.0000380. (Corresponding author)
[26] M.-Y. Cheng and M.-T. Cao*, "Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers," Structure and Infrastructure Engineering, vol. 11, no. 9, pp. 1178-1189, 2015/09/02 2015, doi: 10.1080/15732479.2014.939089. (Corresponding author)
[27] M.-T. Cao, M.-Y. Cheng, and Y.-W. Wu, "Hybrid Computational Model for Forecasting Taiwan Construction Cost Index," Journal of Construction Engineering and Management, vol. 141, no. 4, p. 04014089, 2015/04/01 2015, doi: 10.1061/(ASCE)CO.1943-7862.0000948.
[28] M.-Y. Cheng, M.-T. Cao*, and D.-H. Tran, "A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons," Automation in Construction, vol. 41, pp. 60-69, 2014/05/01/ 2014, doi: https://doi.org/10.1016/j.autcon.2014.02.008. (Corresponding author)
[29] M.-Y. Cheng and M.-T. Cao*, "Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines," Applied Soft Computing, vol. 22, pp. 178-188, 2014/09/01/ 2014, doi: https://doi.org/10.1016/j.asoc.2014.05.015. (Corresponding author)
[30] M.-Y. Cheng and M.-T. Cao*, "Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams," Engineering Applications of Artificial Intelligence, vol. 28, pp. 86-96, 2014/02/01/ 2014, doi: https://doi.org/10.1016/j.engappai.2013.11.001. (Corresponding author)
PUBLICATIONS IN INTERNATIONAL CONFERENCES
[1] K. T. Chang, Y. S. Chiang, M. T. Cao, C. T. Wang, Analyzing Pre- and Post-Earthquake Changes using Optical and SAR Satellite Images, CORECT-IJJSS 2019 International Conference on Sustainability Science and Management: Advanced Technology in Environmental Research, Bali, Indonesia, Nov. 14-15, 2019.
[2] K. T. Chang, M. T. Cao, Estimating Seismic Retrofitting Cost of Taiwan School Buildings Using AI-Based Inference Models, ICEO-SI 2019, 4th Symposium, Taichung, Taiwan, June 23-26, 2019.
[3] Minh-Tu Cao, Nhat-Duc Hoang, Automatic Recognition of Concrete Spall Using Image Processing and Metaheuristic Optimized LogitBoost Classification Tree, The 24th Symposium Construction Engineering and Management, Taipei, Taiwan, August 05th 2020 4.
[4] Minh-Tu Cao, K.T. Chang, Mohammad Adhan, Multiple Dashcam Image Resource-Trained Deep Learning Models for Enhancing Road Damage Detection, The 24th Symposium Construction Engineering and Management, Taipei, Taiwan, August 05th 2020.
[5] Minh-Tu Cao, N.N. Mai, C.C. Chen Smart Ensemble Hyperparameter-free Machine Learner for Predicting the Bond Capacity of an FRP-to-concrete Interface: Multinational Data, The 26th Symposium Construction Engineering and Management, Zhongli, Taiwan, July 22nd 2022.
[6] Minh-Tu Cao, N.N. Mai, C.C. ChenPredicting the Long-Term Deflection of Reinforced Concrete Beams Using Feature Refinement-Based Self-tuning Machine Learning Model, The 26th Symposium Construction Engineering and Management, Zhongli, Taiwan, July 22nd 2022.
[7] 鄭皓中、王世旭、張智安、高明秀、王維志, 運用BIM與電腦視覺及物件偵測技術於工程進度追蹤, 2022第26屆營建工程與管理學術研討會論文集，台灣中壢(中央) (Outstanding Paper Award)
[8] 芫毅 趙, 乃文 紀, 紹偉 翁, 世昕 陳, 裕仁 鄭, 明秀 高, 維志 王, 運用建築資訊模型(BIM)以輔助查核營建工程估驗計價, 2023第27屆營建工程與管理學術研討會論文集，台灣新竹(陽明交大) (Outstanding Paper Award)
[9] 泓志 陳, 文穎 黃, 宗益 蔡, 明秀 高, 世旭 王, 維志 王, 建築工程生命週期階段減碳策略之選擇與評估, 2023第27屆營建工程與管理學術研討會論文集，台灣新竹(陽明交大)
Awarded Research Projects
| 2023/8~2024/7 |
| 2023/8~2024/7 |
| 2022/8~2023/7 |
| 2020/8~2021/7 |
National Taiwan University of Science and Technology – Department of Construction Engineering | |
| Educational qualification: Ph.D. | 2015/6 |
| Dissertation: Artificial Intelligence-Based Inference Support Models for Construction Engineering and Management | |
National Taiwan University of Science and Technology – Department of Construction Engineering | |
| Educational qualification: Master’s degree | 2012/6 |
| Dissertation: Optimization of Project Cost under Time-Quality Requirement Using Advanced Constraint Handling Differential Evolution (ACH-DE) | |
Hanoi University of Civil Engineering - Faculty of Civil and Industrial Construction | |
| Educational qualification: Bachelor | 2010/7 |
2 Vacancies
Job Description
BeeLab 正在招募有動力且具才華的個人加入我們的研究項目，該項目專注於使用先進的人工視覺技術和無人機技術進行即時建築外牆檢測。
此項目結合了最先進的電腦視覺技術、人工智慧（AI）演算法和無人機技術，徹底革新建築外牆的檢測方式，提升檢測效率、準確性和安全性。
工作內容：
作為該創新項目的一部分，候選人將從事以下工作：
- 開發和應用先進的電腦視覺技術，用於識別建築外牆上的結構問題，例如裂縫和瓷磚剝落。
- 整合人工智慧驅動的即時數據處理系統，用於無人機輔助檢測。
- 與前沿無人機技術合作，捕獲高質量的外牆數據並自動化檢測過程。
- 參與設計、測試和實施自動化檢測工作流程，目標是減少人工操作並提高建築檢測的安全性。
我們在尋找：
我們正在尋找熱情的應屆本科生和研究生，具有以下專業背景：
- 人工智慧與電腦視覺： 有機器學習模型、深度學習和圖像處理經驗者優先。
- 無人機技術： 熟悉無人機、自主導航系統和通過空中平台收集數據的經驗者優先。
- 建築工程或結構工程： 具備建築外牆、材料及結構健康監測知識者優先。
- 程式設計與軟體開發： 熟悉 Python、MATLAB 或其他與人工智慧開發和數據分析相關的程式語言者優先。
Preferred Intern Education Level
- Final-year undergraduate students in relevant fields
- Master students
- Ph.D. students
Skill sets or Qualities
- Technical Expertise: Strong understanding of machine learning, computer vision, drone technology, and programming languages (e.g., Python, MATLAB)
- Professional Knowledge: Background and experience in AI applications, or related fields.
- Innovative and Practical Mindset: Willing to experiment with new technologies and apply them to real-world problems, driving research outcomes.
- Team Collaboration: Able to work effectively in an interdisciplinary team environment, contributing expertise and communicating clearly.
- Self-Driven and Eager to Learn: Willingness to independently learn new technologies, tools, and methods, and stay updated on the latest research developments