Vol. 2 No. 2 (2026): October | CEST (Civil Engineering Science and Technology)

Civil Engineering Science and Technology (CEST) is a peer-reviewed journal that applies a double-blind review process to ensure the rigor, credibility, and objectivity of published works. The journal adopts an open-access model, facilitating broad dissemination of scientific contributions to the global academic and professional community. Published biannually in March and October, CEST focuses on scientific approaches, engineering methods, and technological innovations across key areas of civil engineering, including structural engineering, construction materials, transportation and infrastructure systems, environmental sustainability, and the integration of emerging digital technologies such as artificial intelligence, computer vision, Internet of Things, Building Information Modeling, and digital twin.

Volume 2, Number 2 (October 2026) presents contributions from authors across Indonesia, Denmark, the Netherlands, the United Kingdom, Ireland, and Canada, reflecting the journal’s continued development toward a broader international research network. This issue features studies on 3D-printed geopolymer concrete reinforced with recycled carbon fibers, bio-cemented concrete incorporating industrial by-products for low-carbon infrastructure, UAV-based structural crack detection using vision-language models, digital twin applications for structural health monitoring and remaining useful life prediction, and the relationship between urban road accessibility and healthcare utilization. Additional contributions explore Vision AI–integrated 4D BIM for real-time construction progress monitoring, physics-informed digital twins for climate-induced degradation of coastal bridges, AI-based pavement distress classification and degradation mapping, and hybrid Vision-AI and geospatial frameworks for multimodal infrastructure assessment. Overall, this issue highlights the growing convergence of advanced materials, intelligent monitoring, digital twins, computer vision, geospatial analysis, and data-driven engineering in supporting more resilient, sustainable, and adaptive civil infrastructure systems.