All Research Papers Published In Automation in Construction

Research Article
10.1016/j.autcon.2026.107005

Semi-automated high-accuracy 3D mapping of concrete arch bridges using UAV photogrammetry and intelligently optimized ground control point layouts

Aug 01, 2026
Automation in Construction
Lian-Heng Cai + 3 more +3

Research Article
10.1016/j.autcon.2026.106924

Tunnel scanner: Geometry-informed synthetic point cloud generation and transfer learning for tunnel segmentation

Jul 01, 2026
Automation in Construction
Wanru Yang + 5 more +5

Structural health monitoring of underground tunnels increasingly uses advanced sensing and data-driven methods. Laser-scanned 3D point clouds capture spatially rich measurements of segmental tunnel linings and require segmentation as a prerequisite for downstream analysis. Deep learning (DL) is effective for point-cloud segmentation, but scarce datasets and costly annotation limit practical use. This paper presents Tunnel Scanner , a high-fidelity simulator that synthesises realistic tunnel point clouds with automatic annotation. The plug-and-play module Hybrid Position–Normal Local Spatial Encoding embeds geometric priors into DL backbones and combines with transfer learning (TL) to exploit synthetic data for domain adaptation. Models trained only on synthetic data achieved 71.9% mean Intersection-over-Union (mIoU) and 86.6% Overall Accuracy (OA), and TL increased performance to at least 78.8% mIoU and 90.9% OA with limited real data. This paper highlights geometry-informed data synthesis as a viable augmentation approach for digital inspection and asset management of large-scale tunnels. • Develop a high-fidelity simulator to address the scarcity of tunnel point clouds. • Propose a plug-and-play module encoding geometric features into DL backbones. • Investigate transfer learning to enhance 3D Sim-to-Real domain adaptation. • The end-to-end framework yields ＋24% segmentation accuracy with limited real data.

Research Article
1
10.1016/j.autcon.2026.106905

BIM–LLM integration in AECO workflows: Applications, validation, and future directions

Jul 01, 2026
Automation in Construction
Ingeon Park + 3 more +3

Research Article
10.1016/j.autcon.2026.106878

Integrating spatial and structural considerations in floor plan transformations of historic masonry buildings

Jul 01, 2026
Automation in Construction
Daniele M.s Paulino + 3 more +3

Research Article
10.1016/j.autcon.2026.106886

Multi-agent integration of LLMs and graph-based method for automated MEP constructability checking

Jul 01, 2026
Automation in Construction
Ang Li + 6 more +6

Research Article
10.1016/j.autcon.2026.106928

Comprehensive review of AI-driven models for sewer condition assessment with defect analysis

Jul 01, 2026
Automation in Construction
Shihui Ma + 4 more +4

Research Article
10.1016/j.autcon.2026.106940

Deep reinforcement learning enhanced adaptive path planning in synchronous TBM deviation correction

Jul 01, 2026
Automation in Construction
Limao Zhang + 4 more +4

Research Article
10.1016/j.autcon.2026.106938

Controllable flow matching synthesis for generalizable crack segmentation with a 50k image–mask benchmark

Jul 01, 2026
Automation in Construction
Babak Asadi + 3 more +3

Research Article
10.1016/j.autcon.2026.106892

Integrating machine learning and multi-agent deep reinforcement learning for performance-driven urban design

Jun 01, 2026
Automation in Construction
Zewei Shi + 4 more +4

Research Article
10.1016/j.autcon.2026.106877

Augmented reality for positioning embedded components in precast concrete: Device comparison and task performance

Jun 01, 2026
Automation in Construction
Zhidong Xu + 4 more +4