Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR) in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting underground explosive artifacts. A GPR survey was conducted on a designed scenario that included the most commonly buried items in historic battle fields, such as mines, projectiles and mortar grenades. The buried targets were identified using both frequencies, although the higher vertical resolution provided by the 2.3-GHz antenna allowed for better recognition of the reflection patterns. The targets were also detected automatically using machine learning techniques. Neural networks and logistic regression algorithms were shown to be able to discriminate between potential targets and clutter. The neural network had the most success, with accuracies ranging from 89% to 92% for the 1-GHz and 2.3-GHz antennas, respectively.

Ground-penetrating radar (GPR) has proved to be a valuable method for tunneling quality management and evaluation. The research literature includes many successful works addressing the use of this technique with the aim to detect the existence of internal pathologies and anomalies. However, most of them have been carried out on tunnels in operation. In these situations, the identification of inner elements and possible damage has been severely limited due to a shallower rebar mesh that complicates the radar data interpretation process. Therefore, this paper presents the evaluation of an excavated high-speed railway tunnel during the early stages of construction instead of considering a finished one. The purpose of the study was to demonstrate the potential of this method when applied during these first critical phases. The results provided noteworthy information: namely, thickness of the gunite layer, lining voids and delamination, moisture problems, and the presence of reinforcing in the tunnel l...

Some subjects of Engineering Mechanical result particularly difficult because previous knowledge and skills are required. It is the case of Graphic Expression. There are two main reasons justifying failure. Firstly, it is very common to find students with difficulties for the spatial vision. Secondly, a variable percentage of students do not have any previous experience about the contents of this subject since it is a non-compulsory subject in secondary school. This work presents different experiences of inclusive teaching to help these students to achieve the required level of knowledge and the evaluation of this activities.