Abstracts – Browse Results
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Birgonul, Z (2021) A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information. Smart and Sustainable Built Environment, 10(3), 504-35.
Brandín, R and Abrishami, S (2021) Information traceability platforms for asset data lifecycle: blockchain-based technologies. Smart and Sustainable Built Environment, 10(3), 364-86.
Eiris, R, Albeaino, G, Gheisari, M, Benda, W and Faris, R (2021) InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection. Smart and Sustainable Built Environment, 10(3), 438-56.
Faris, E, Matarneh, S, Talebi, S, Kagioglou, M, Hosseini, M R and Abrishami, S (2021) Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review. Smart and Sustainable Built Environment, 10(3), 345-63.
Hosseini, M R, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W and Nikmehr, B (2021) Position paper: digital engineering and building information modelling in Australia. Smart and Sustainable Built Environment, 10(3), 331-44.
Karsten Winther, J, Nielsen, R, Schultz, C and Teizer, J (2021) Automated activity and progress analysis based on non-monotonic reasoning of construction operations. Smart and Sustainable Built Environment, 10(3), 457-86.
Lamptey, T, De-Graft, O-M, Acheampong, A, Adesi, M and Ghansah, F A (2021) A framework for the adoption of green business models in the Ghanaian construction industry. Smart and Sustainable Built Environment, 10(3), 536-53.
Mahmoudi, E, Stepien, M and König, M (2021) Optimisation of geotechnical surveys using a BIM-based geostatistical analysis. Smart and Sustainable Built Environment, 10(3), 420-37.
- Type: Journal Article
- Keywords: geotechnical survey; soil investigations; environment models; boreholes; accuracy; experimental design; underground construction; uncertainty; simulation; knowledge management; geographic information systems; building information modeling
- ISBN/ISSN:
- URL: http://dx.doi.org/10.1108/SASBE-03-2021-0045
- Abstract:
A principle prerequisite for designing and constructing an underground structure is to estimate the subsurface's properties and obtain a realistic picture of stratigraphy. Obtaining direct measure of these values in any location of the built environment is not affordable. Therefore, any evaluation is afflicted with uncertainty, and we need to combine all available measurements, observations and previous knowledge to achieve an informed estimate and quantify the involved uncertainties. This study aims to enhance the geotechnical surveys based on a spatial estimation of subsoil to customised data structures and integrating the ground models into digital design environments. The present study's objective is to enhance the geotechnical surveys based on a spatial estimation of subsoil to customised data structures and integrating the ground models into digital design environments. A ground model consisting of voxels is developed via Revit-Dynamo to represent spatial uncertainties employing the kriging interpolation method. The local arrangement of new surveys are evaluated to be optimised. The visualisation model's computational performance is modified by using an octree structure. The results show that it adapts the structure to be modelled more efficiently. The proposed concept can identify the geological models' risky locations for further geological investigations and reveal an optimised experimental design. The modifications criteria are defined in global and local considerations. It provides a transparent and repeatable approach to construct a spatial ground model for subsequent experimental or numerical analysis. In the first attempt, the ground model was discretised by a grid of voxels. In general, the required computing time primarily depends on the size of the voxels. This issue is addressed by implementing octree voxels to reduce the computational efforts. This applies especially to the cases that a higher resolution is required. The investigations using a synthetic soil model showed that the developed methodology fulfilled the kriging method's requirements. The effects of variogram parameters, such as the range and the covariance function, were investigated based on some parameter studies. Moreover, a synthetic model is used to demonstrate the optimal experimental design concept. Through the implementation, alternative locations for new boreholes are generated, and their uncertainties are quantified. The impact of the new borehole on the uncertainty measures are quantified based on local and global approaches. For further research to identify the geological models' risky spots, the development of this approach with additional criteria regarding the search neighbourhood and consideration of barriers and trends in real cases (by employing different interpolation methodologies) should be considered.
Oke, A E and Arowoiya, V A (2021) Evaluation of internet of things (IoT) application areas for sustainable construction. Smart and Sustainable Built Environment, 10(3), 387-402.
Xiong, R and Tang, P (2021) Machine learning using synthetic images for detecting dust emissions on construction sites. Smart and Sustainable Built Environment, 10(3), 487-503.