BIM has become an intrinsic tool in managing building projects due to its ability to comprehensively represent information in digital form. However, using BIM as an information exchange tool is still in its infancy, particularly with regard to construction progress monitoring beyond time schedule information. The current study focuses on the development of an automated progress monitoring framework based on an IFC-based BIM and provides an extensive methodology based on a structured task-based approach in accordance with the latest IFC4.x schema in four stages. The first stage creates the appropriate IFC entities, which are then enriched with their values in the second stage. The third stage integrates the actual progress information, which requires regular updating from the construction site. Finally, the fourth stage enables the retrieval of progress information, which is then reported in a user-friendly format along with the estimation of additional progress indicators. The proposed method successfully integrated the progress information into their IFC-based BIM models, demonstrating its practical use for monitoring construction progress. In the end, a web-based application was also developed that made use of progress information stored within the standardized hierarchy of the updated IFC-based BIM to facilitate efficient reporting.

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute).

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.