An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Surrogate modelling has been used successfully to alleviate the computational burden that results from high-fidelity numerical models of seawater intrusion in simulation-optimization routines. Nevertheless, little attention has been given to multi-fidelity modelling methods to address cases where only limited runs with computationally expensive seawater intrusion models are considered affordable imposing a limiting factor for single-fidelity surrogate-based optimization as well. In this work, a new adaptive multi-fidelity optimization framework is proposed based on co-Kriging surrogate models considering two model fidelities of seawater intrusion. The methodology is tailored to the needs of solving pumping optimization problems with computationally expensive constraint functions and utilizes only small high-fidelity training datasets. Results from both hypothetical and real-world optimization problems demonstrate the efficiency and practicality of the proposed framework to provide a steep improvement of the objective function while it outperforms a comprehensive single-fidelity surrogate-based optimization method. The method can also be used to locate optimal solutions in the region of the global optimum when larger high-fidelity training datasets are available

Comparison of Sharp Interface to Variable Density Models in Pumping Optimisation of Coastal Aquifers

A number of models have been developed to simulate seawater intrusion in coastal aquifers, which differ in the accuracy level and computational demands, based on the approximation level of the application. In this paper, four seawater intrusion models are employed to calculate the optimal pumping rates in a coastal aquifer management problem. The first model considers both fluid flow and solute transport processes and assumes a variable-density transition zone between saltwater and freshwater. The implementation of the model in simulation-optimisation routines is impractical, due to the computational time required for the simulation. The second model neglects the dispersion mechanism and assumes a sharp interface between saltwater and freshwater. The sharp interface model is significantly faster than the variable density model, however, it may introduce errors in the estimation of the seawater intrusion extent. The remaining two models are modifications of the second model, which intent to correct the inaccuracies of the simplified sharp interface approximation. All four models are utilised to simulate an unconfined coastal aquifer with multiple pumping wells and an optimisation method is used to calculate the maximum allowed pumping rates. The optimisation results are then analysed, in order to examine if the three sharp interface models could provide feasible solutions in the area of the variable density optimum, which is considered as a benchmark solution

AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure