Α mass-reduction design concept for seismic hazard mitigation

A mass reduction concept for seismic hazard mitigation is investigated herein. The proposed method is implemented through floating slabs, ie, slabs that have been seismically isolated from the skeleton of the structure. The investigation is based on time history analyses of MDOF models under scaled strong-motion seismic records complying with an EC8 spectrum. The purpose of these slabs is twofold; for selected short isolation periods, they act as a mass-damping system for the overall response of the structure, employing significantly more mass than traditional TMDs, while for longer isolation periods they provide seismic protection on their contents while effectively reducing the seismic mass of the structure. In the latter case, it is found that the response of the skeleton can be evaluated accurately from a corresponding reduced-mass model. The proposed design method does not necessarily aim at replacing existing seismic design approaches; it rather provides design versatility in the hands of the practicing engineers.     

Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models

Pumping optimization of coastal aquifers involves complex numerical models. In problems with many decision variables, the computational burden for reaching the optimal solution can be excessive. Artificial Neural Networks (ANN) are flexible function approximators and have been used as surrogate models of complex numerical models in groundwater optimization. However, this approach is not practical in cases where the number of decision variables is large, because the required neural network structure can be very complex and difficult to train. The present study develops an optimization method based on modular neural networks, in which several small subnetwork modules, trained using a fast adaptive procedure, cooperate to solve a complex pumping optimization problem with many decision variables. The method utilizes the fact that salinity distribution in the aquifer, depends more on pumping from nearby wells rather than from distant ones. Each subnetwork predicts salinity in only one monitoring well, and is controlled by relatively few pumping wells falling within certain control distance from the monitoring well. While the initial control area is radial, its shape is adaptively improved using a Hermite interpolation procedure. The modular neural subnetworks are trained adaptively during optimization, and it is possible to retrain only the ones not performing well. As optimization progresses, the subnetworks are adapted to maximize performance near the current search space of the optimization algorithm. The modular neural subnetwork models are combined with an efficient optimization algorithm and are applied to a real coastal aquifer in the Greek island of Santorini. The numerical code SEAWAT was selected for solving the partial differential equations of flow and density dependent transport. The decision variables correspond to pumping rates from 34 wells. The modular subnetwork implementation resulted in significant reduction in CPU time and identified an even better solution than the original numerical model.     

Digital simulation of multivariate two- and three-dimensional stochastic processes with a spectral turning bands method

The space domain version of the turning bands method can simulate multidimensional stochastic processes (random fields) having particular forms of covariance functions. To alleviate this limitation a spectral representation of the turning bands method in the two-dimensional case has shown that the spectral approach allows simulation of isotropic two-dimensional processes having any covariance or spectral density function. The present paper extends the spectral turning bands method (STBM) even further for simulation of much more general classes of multidimensional stochastic processes. Particular extensions include: (i) simulation of three-dimensional processes using STBM, (ii) simulation of anisotropic two- or three-dimensional stochastic processes, (iii) simulation of multivariate stochastic processes, and (iv) simulation of spatial averaged (integrated) processes. The turning bands method transforms the multidimensional simulation problem into a sum of a series of one-dimensional simulations. Explicit and simple expressions relating the cross-spectral density functions of the one-dimensional processes to the cross-spectral density function of the multidimensional process are derived. Using such expressions the one-dimensional processes can be simulated using a simple one-dimensional spectral method. Examples illustrating that the spectral turning bands method preserves the theoretical statistics are presented. The spectral turning bands method is inexpensive in terms of computer time compared to other multidimensional simulation methods. In fact, the cost of the turning bands method grows as the square root or the cubic root of the number of points simulated in the discretized random field, in the two- or three-dimensional case, respectively, whereas the cost of other multidimensional methods grows linearly with the number of simulated points. The spectral turning bands method currently is being used in hydrologic applications. This method is also applicable to other fields where multidimensional simulations are needed, e.g., mining, oil reservoir modeling, geophysics, remote sensing, etc.     

Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers

Single and multiple surrogate models were compared for single-objective pumping optimization problems of a hypothetical and a real-world coastal aquifer. Different instances of radial basis functions and kriging surrogates were utilized to reduce the computational cost of direct optimization with variable density and salt transport models. An adaptive surrogate update scheme was embedded in the operations of an evolutionary algorithm to efficiently control the feasibility of optimal solutions in pumping optimization problems with multiple constraints. For a set of independent optimization runs, results showed that multiple surrogates, either by selecting the best or by using ensembles, did not necessarily outperform the single surrogate approach. Nevertheless, the ensemble with optimal weights produced slightly better results than selecting only the best surrogates or applying a simple averaging approach. For all cases, the computational cost, by using single or multiple surrogate models, was reduced by up to 90% of the direct optimization.