Land Transport Authority

Three-dimensional (3D) geological model, as an effective tool for modelling the complex nature of underground geological conditions, has been used for the design and construction of underground facilities. The accuracy and reliability of the geological information reflected by the 3D model are critical for engineering practitioners to make sound judgement. It is important to consistently update the 3D geological model with new borehole or geophysical data for better assessment of the underground uncertainties. In this study, an application of the NTU-BCA 3D geological model to an undergoing subway project in Singapore has been investigated. The established 3D geological model was updated using the new borehole data obtained in the project. A comparison of the virtual boreholes generated in the 3D model with the real borehole data in terms of rockhead positions was made as a verification of the uncertainties of the model. The uncertainties analyses show that the standard deviations of the predicted errors for the rockhead are improved after the new borehole data was incorporated into the model. Predictions made by spatial statistical analyses are also presented and compared with the borehole data and the results from the 3D geological modeling. Through the study, an example for using newly obtained borehole data to update the existed 3D geological model to improve the accuracy was given.

Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing collected data from archived projects can assist managers to envisage project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average – ARIMA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next 3 months. This research paper provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short term project progress in order to take proactive measures on the different influencing factors.

Shallow slope failures due to rainfall commonly occur in residual soil, especially in tropical areas like Singapore. Therefore, it is critical to understand the distribution of residual soil properties throughout Singapore Island. Proper procedures are required for selection of appropriate locations of soil sampling to obtain the representative soil properties. The aim of this study is to establish the necessary procedures and methods which are applicable to select suitable locations for soil sampling in Singapore. In this study, the Geographical Information System (GIS) with the incorporation of three layers: digital elevation model (DEM), slope angle, and the soil sampling locations from past studies were used to generate suitability map for determination of soil sampling locations in Singapore. Five suitability classes were implemented in each layer: very low, low, medium, high, and very high. Four types of spatial analyses such as "Analysis by weights", "Inference matrix method", "Fuzzy Overlay Gamma method", and "per-cell statistic maximum method (PCSM)" were assessed to identify the appropriate method for determination of the suitable locations for soil sampling in Singapore. These analyses were carried out using Spatial Analyst Tools in ArcGIS environment. The results of this study indicated that the spatial analysis by weights and Fuzzy Overlay Gamma are suitable for determination of soil sampling locations up to 100 data points. The spatial analysis using the inference matrix is suitable for determination of limited number of soil sampling locations. The spatial analysis using the PCSM method is suitable for determination of large number of soil sampling locations. The findings from this study will benefit the practical engineers in surveying as well as other related researchers for determination of suitable locations of soil sampling.

There is a recent effort at One-North key signalized intersections to setup DSRC road-side beacon infrastructure to support Singapore Autonomous Vehicles (AV) trials. Unlike conventional AVs that operate in independent sensing mode, the notion of employing DSRC-equipped beacons is to augment AVs' operation and offer key benefits such as improved situational awareness and sensing-range extension. In this paper, we present the design and implementation of a DSRC-based road-side beaconing system, called BESAFE, for allowing real-time information sharing with AVs. BESAFE framework supports the dissemination of: (i) traffic light status, (ii) GNSS positioning augmentation, (iii) surrounding traffic information, and (iv) collection of vehicle probe data and node infrastructure status. A key design principle of BESAFE is the full compliance of international standards of DSRC/WAVE, IEEE 1609.x, and SAE J2735, so as to ensure interoperability between infrastructure provider and different AV operators. Several important protocol design considerations have been discussed, including the software architecture, service discovery via WSA, application layer messaging, frame routing and node status reporting. To assure different AV operators to use the common test-bed infrastructure, the basic BESAFE framework is also extended by incorporating security mechanism of enhanced encryption and frame signing.