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Now showing items 11-14 of 14
Rapid earthquake loss assessment based on machine learning and representative sampling
(Earthquake Spectra, 2021)
This paper proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A Random Forest classification model predicts a ...
Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions
(Blue-Green Systems, 2023)
The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, ...
Compensating the lack of big data in construction industry with expert knowledge: a case study
(1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2022, 2022)
Due to various reasons, there is a lack of big data in the construction industry, one of the main obstacles to a broader implementation of AI. Another obstacle is adhering to analytical methods in fields more suitable for ...
Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge
(Sustainability, 2022)
Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of ...