Paper Collection of AI/ML + Architectural Engineering (AEC)

This is research papers of architectural engineering based AI/ML techniques.

AI/ML + Urban Planning & Real Estate

• Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning by Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018).
• Real estate value prediction using linear regression by Ghosalkar, N. N., & Dhage, S. N. (2018).

AI/ML + Indoor Quality & Environment

• Machine learning and statistical models for predicting indoor air quality by Wei, W., Ramalho, O., Malingre, L., Sivanantham, S., Little, J. C., & Mandin, C. (2019).
• Machine learning methods to forecast temperature in buildings by Mateo, F., Carrasco, J. J., Sellami, A., Millán-Giraldo, M., Domínguez, M., & Soria-Olivas, E. (2013).

AI/ML + HVAC

• Regression-based approach to modeling emerging HVAC technologies in EnergyPlus: A case study using a Vuilleumier-cycle heat pump by Woods, J., & Bonnema, E. (2019).
• Energy efficient building HVAC control algorithm with real-time occupancy prediction by Shi, J., Yu, N., & Yao, W. (2017).
• Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning by Du, Y., Zandi, H., Kotevska, O., Kurte, K., Munk, J., Amasyali, K., ... & Li, F. (2021).

AI/ML + Construction Management

• A multi‐objective genetic algorithm strategy for robust optimal sensor placement by Civera, M., Pecorelli, M. L., Ceravolo, R., Surace, C., & Zanotti Fragonara, L. (2020).
• Simulation-based construction productivity forecast using neural-network-driven fuzzy reasoning by Mirahadi, F., & Zayed, T. (2016).
• Supervised vs. unsupervised learning for construction crew productivity prediction by Oral, M., Oral, E. L., & Aydın, A. (2012).
• Convolutional neural networks: Computer vision-based workforce activity assessment in construction by Luo, H., Xiong, C., Fang, W., Love, P. E., Zhang, B., & Ouyang, X. (2018).
• Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach by Fang, W., Ding, L., Zhong, B., Love, P. E., & Luo, H. (2018).

AI/ML + Structure

• Pixel‐level multicategory detection of visible seismic damage of reinforced concrete components. Computer‐Aided Civil and Infrastructure Engineering by Miao, Z., Ji, X., Okazaki, T., & Takahashi, N. (2021).

AI/ML + Policy

AI/ML + Civil

• The development of Gaussian process regression for effective regional post‐earthquake building damage inference by Sheibani, M., & Ou, G. (2020).

DigitalTwin

• Towards a semantic Construction Digital Twin: Directions for future research by Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020).