Претраживање
Приказ резултата 61-66 од 66
Broadening the urban sustainable energy diapason through energy recovery from waste: A feasibility study for the capital of Serbia
(Elsevier Ltd, 2017)
Metropolitan areas are large consumers of energy and there is a growing need to broaden the urban sustainable energy diapason and increase the share of renewable and sustainable energy in overall energy consumption. This ...
Application of artificial neural networks for hydrological modelling in karst
(Union of Croatian Civil Engineers and Technicians, 2018)
The possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural ...
The Successful Delivery of Megaprojects: A Novel Research Method
(SAGE Publications Inc., 2017)
Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project ...
Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains
(Springer, 2023)
This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number ...
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 ...
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 ...