AI in Civil Engineering

Aims & Scope

AI in Civil Engineering ( AICE ) is an international journal for publishing original research papers, reviews, comments, and perspectives on Artificial Intelligence (AI) applications in civil engineering. The journal accepts a broad spectrum of articles regarding basic and applied research of advanced AI technologies in the domain of civil engineering, such as buildings, infrastructure, construction, and maintenance. AICE aims to become a knowledge hub to capture and archive significant progress in AI-related civil engineering and is dedicated to advancing the frontiers of AI-enabled knowledge discovery and technological innovation in the field of civil engineering. The journal publishes refereed research papers on all aspects pertaining to digital and computer technology in civil engineering, with a particular focus on the advances and applications of AI. The scope and forthcoming issues include, but are not limited to: Civil engineering objectives Structural engineering (optimization, health monitoring); Infrastructure engineering (bridges, tunnels, pavements); Bridge engineering; Geotechnical engineering (soil behavior, slope stability); Underground engineering (tunnel inspection); Transportation (travel prediction, icing detection); Construction (robotic repair, automated monitoring); Environmental engineering & Hydrology (hydraulic modeling); Disaster mitigation (earthquake risk); Facility management; Sustainable engineering (green materials); Architecture (generative AI) Project life-cycle phases Engineering design (topology optimization); BIM (planning/construction/operations); Computational mechanics; Materials development (concrete prediction, 3D printing); Intelligent construction (quality, schedule, safety, health and environment management); Disaster resilience (liquefaction, epidemic forecasting); Life-cycle assessment; Structural Health Monitoring Different scales Livable buildings; Districts; Communities; Cities; Society; City Information Modelling (C

Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.

Source: OpenAlex · Each year’s green bar = citations earned by that year’s papers, counted to date — so recent years look lower simply because their papers haven’t had time to be cited yet.

SJR Quartile by Discipline

Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.

Subject Classification