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The construction sector in Greece has been developing radically in the field of building renovations. The foremost problem for projects in the building construction industry is producing an accurate and reliable cost estimate at the onset of construction. The artificial neural network (AΝΝ) approach, using data available at the early stages of the project, can help resolve or prevent any kind of difficulty that could make the successful completion of a building less likely. ANNs have been highly efficient in gaining results which could prevent the failure of building constructions projects. The ultimate goal is to highlight the usefulness of the adoption of ANNs models to predict the final cost of a building renovation project. Thus, construction companies could avoid financial failure, provided that the gap between cost prediction and final cost for renovation projects is minimized. This paper presents an artificial neural network (ANN) approach for predicting renovation costs in Greek construction projects. The study, based on a comprehensive literature review and real renovation data from construction companies, employs IBM SPSS Statistics software to build, train, and test the ANN model. The model, which uses initial cost, estimated time, and initial demolition cost as inputs, is based on the radial basis function procedure. The model presents high performance with up to 2% sum of squares error and near zero relative error, demonstrating the ANN's effectiveness in estimating total renovation costs. [ABSTRACT FROM AUTHOR]

Ensuring sea surface tranquility within port basins is of paramount importance for safe and efficient port operations and vessels' accommodation. The present study aims to introduce a robust numerical model based on mild-slope equations, capable of accurately simulating wave disturbance and resonance in ports. The model is further enhanced by the integration of an artificial neural network (ANN) to address partial reflection, and its efficiency is optimized by developing a parallel algorithm based on OpenMP, allowing for a reduction in the required simulation times for real port areas spanning several kilometers horizontally. Numerous numerical experiments focusing on wave reflection against a vertical wall were conducted to develop the ANN. This neural network was designed to determine the appropriate value of the eddy viscosity coefficient, a crucial parameter in the momentum equation of the mild-slope model, tailored to incident wave characteristics. The model's validity was confirmed through rigorous validation against experimental measurements, covering wave disturbance, rectangular harbor resonance, and Bragg resonance. The model consistently demonstrated a more than satisfactory performance across all considered scenarios. In a practical application, the model was deployed in the Port of Rethymno, Crete Island, Greece, effectively capturing and describing dominant phenomena within the port area. The implementation of a parallel algorithm significantly reduced the simulation times by ~92%, compared to the serial algorithm, thereby enhancing the model's efficiency and applicability in real-world port environments. [ABSTRACT FROM AUTHOR]