t. celik

Nowadays, construction activities and projects are becoming much more challenging and complex to handle. Being involve with different stakeholders is a difficult task and information and communication technology (ICT) has been addressed as a solution. Afterwards, there was a major shift in ICT usage for construction projects and furthermore, Building Information Modeling (BIM) has been found its position among the experts as a Computer Aided Design (CAD) paradigm. By inspiration from modality of BIM, there can be an adoption to other sub-branch of construction industry such as infrastructure domain. Since BIM has proven benefits in buildings, infrastructure projects might gain similar advantages through proper implementation. This new dimension can be called Infrastructure Building Information Modeling (I-BIM). The main aim of conducting this research is to identify and prove the existing lack of using BIM for infrastructure projects. In order to fulfill this gap, questionnaire survey designed and respondents were mostly located at United States of America and Turkey. As a finding, authors clarify the preponderance and impediment components in the path of I-BIM utilization and discuss five main categories with total number of 26 variables. Furthermore data mining among selected countries has been done for all variables with comprehensive discussion.

The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing has made it a suitable alternative for mapping of the geochemical elements using satellite spectral reflectance. In this research work, 407 surface stream sediment samples of the zinc (Zn) and lead (Pb) elements are collected from Central Wales. Five machine learning models, namely the Support Vector Regression (SVR), Generalized Linear Model (GLM), Deep Neural Network (DNN), Decision Tree (DT), and Random Forest (RF) regression, are applied for prediction of the Zn and Pb concentrations using the Sentinel-2 satellite multi-spectral images. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values.