Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

For green building design, this paper proposes a multi-objective optimization (MOO)
framework to properly adjust design parameters using a deep reinforcement learning (DRL) …

Advances in tunnel boring machines (TBM) have leveraged applied artificial intelligence to
promote sustainable and automatic tunneling construction. This paper highlights the …

This study proposes a machine learning-driven approach for the analysis of the feature
importance of seismic parameters on tunnel damage and seismic fragility prediction. The …

The construction engineering and management (CEM) domain frequently meets complex
tasks due to the unavoidable complicated operation environments and the involvement of …

Decision-making in construction planning and scheduling is complex because of budget
and resource constraints, uncertainty, and the dynamic nature of construction environments …

Abstract Machine learning algorithms, including supervised and unsupervised learning
ones, have been widely used in mineral prospectivity map**. Supervised learning …

Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost,
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …

Reliable and timely prediction of the shield tunneling path is essential to avoid deviation and
successfully complete a tunneling project. This study presents a reinforcement learning …

Emerging studies are utilizing reinforcement learning (RL) and imitation learning (IL) to
control large-scale robots in heavy construction tasks. There is limited attention given to the …