Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time

Generative Design of Physical Objects using Modular Framework

In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to promising outlook. However, existing approaches are limited by the specificity of problem under consideration. In addition, they do not provide desired flexibility. In this paper we formulate general approach to an arbitrary generative design problem and propose novel framework called GEFEST (Generative Evolution For Encoded STructure) on its basis. The developed approach is based on three general principles: sampling, estimation and optimization. This ensures the freedom of method adjustment for solution of particular generative design problem and therefore enables to construct the most suitable one. A series of experimental studies was conducted to confirm the effectiveness of the GEFEST framework. It involved synthetic and real-world cases (coastal engineering, microfluidics, thermodynamics and oil field planning). Flexible structure of the GEFEST makes it possible to obtain the results that surpassing baseline solutions

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Integrated life-cycle cost & risk optimization framework for coastal protection structures

While extensive research has been carried out on the management of various types of infrastructure assets, limited research has been carried out for coastal structures. The rapid growth of the world population living in low-lying areas within close range to the shoreline over the past century compounded by the impact of global climate change on shoreline hydrodynamics; have increased the importance of coastal infrastructure management. Climate change has recently increased storm intensities in addition to decreasing storm return periods; imposing greater risks to life and property. The aim of this research is to provide an artificial-intelligence-based framework for coastal protection structures, which is capable of predicting structural deterioration patterns, and accordingly offers the end user the capability of optimization of repair, maintenance, and rehabilitation costs, in addition to the optimization of risk exposure limits under pre-defined budgetary constraints. For this purpose, an Asset Inventory Database (AID) for coastal assets is developed, comprising the design, environmental, and historical data pertaining to coastal assets. Established visual inspection and condition rating procedures are followed to obtain the values for the Structural Condition Index (SI) and a Structural Condition Matrix (SCM) for individual structures, considering a single inspection point. This takes into account cases where no previous inspection and condition rating records are available. SI’s are in their turns classified into severity ranges. Functional Condition Indices (FI\u27s) are also calculated for submerged structures that could not be visually inspected and taken as the equivalent to the Condition Index (CI). Deterioration Transition Matrices (DTM\u27s), including transition probabilities between each of the deterioration severity ranges are next calculated using backward Markov-Chain (MC) analysis. Such probabilities are then utilized to formulate the Markovian Deterioration Transition Matrix (DTM) for each individual sub-reach and hence each individual structure; enabling the prediction of future deterioration. The trends obtained from this forward Markovian deterioration modeling are approximated by mathematical functions using best-fit regression. The single-time deterioration effect of design and intermediate storms is also considered by virtue of the Storm Simulator feature. By calculating the average maintenance and repair per meter run of every coastal structure, corresponding to the condition of the structure, a Genetic-Algorithm (GA) – based Life-Cycle Cost (LCC) optimization modeling is then developed with the aim to minimize the total LCC for the entire coastal assets up to year 2050, while achieving the minimum reliability of structures, expressed as a Priority Index (PI). PI\u27s are numerical values that are factors in the condition state of the structure and its criticality with respect to risk to life and property upon failure. In parallel, another optimization module aims at minimizing the total risk exposure level under various budget scenarios. Both the LCC and risk optimization modules were run for various scenarios of storm occurrences to account for the effect of global climate change. The considered case study in this research is a group of 43 different structures in Alexandria, Egypt. It was found that under stringent climatic conditions, the required LCC to maintain coastal structures at the desired level of reliability increases dramatically as opposed to normal climatic conditions. In addition, it was observed that the risk to life and property decreases with the increase of available budget for maintenance and repair. Further, the suggested framework was observed to be more cost-efficient than the common maintenance and repair strategies, in terms of keeping the maximum acceptable PI threshold