Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

Robust Estimation of Deformation from Observation Differences Using Some Evolutionary Optimisation Algorithms

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.

A rapid-prototyped moulding system towards optimised scalable fabrication of elastomeric microfluidic devices

3D printing and makerspace technologies are increasingly explored as alternative techniques to soft lithography for making microfluidic devices, and for their potential to segue towards scalable commercial fabrication. Here we considered the optimal application of current benchtop 3D printing for microfluidic device fabrication through the lens of lean manufacturing and present a straightforward but robust rapid prototyped moulding system that enables easy estimation of more precise quantities of polydimethylsiloxane (PDMS) required per device to reduce waste and importantly, making devices with better defined depths and volumes for (i) modelling gas exchange and (ii) fabrication consistency as required for quality-controlled production. We demonstrate that this low-cost moulding step can enable a 40 – 300% reduction in the amount of PDMS required for making individual devices compared to the established method of curing approximately 30 grams of PDMS prepolymer overlaid on a 4” silicon wafer master in a standard plastic petri dish. Other process optimisation techniques were also investigated and are recommended as readily implementable changes to current laboratory and foundry-level microfluidic device fabrication protocols for making devices either out of PDMS or other elastomers. Simple calculators are provided as a step towards more streamlined, software controlled and automated design-to-fabrication workflows for both custom and scalable lean manufacturing of microfluidic devices.

A rapid-prototyped moulding system towards optimised scalable fabrication of elastomeric microfluidic devices

3D printing and makerspace technologies are increasingly explored as alternative techniques to soft lithography for making microfluidic devices, and for their potential to segue towards scalable commercial fabrication. Here we considered the optimal application of current benchtop 3D printing for microfluidic device fabrication through the lens of lean manufacturing and present a straightforward but robust rapid prototyped moulding system that enables easy estimation of more precise quantities of polydimethylsiloxane (PDMS) required per device to reduce waste and importantly, making devices with better defined depths and volumes for (i) modelling gas exchange and (ii) fabrication consistency as required for quality-controlled production. We demonstrate that this low-cost moulding step can enable a 40 – 300% reduction in the amount of PDMS required for making individual devices compared to the established method of curing approximately 30 grams of PDMS prepolymer overlaid on a 4” silicon wafer master in a standard plastic petri dish. Other process optimisation techniques were also investigated and are recommended as readily implementable changes to current laboratory and foundry-level microfluidic device fabrication protocols for making devices either out of PDMS or other elastomers. Simple calculators are provided as a step towards more streamlined, software controlled and automated design-to-fabrication workflows for both custom and scalable lean manufacturing of microfluidic devices.

BUCKLING AND POST-BUCKLING OF ISOTROPIC AND COMPOSITE STIFFENED PANELS: A REVIEW ON OPTIMISATION (2000-2015)

Stiffened panels made out of isotropic or anisotropic materials are being extensively used as structural elements for aircraft, maritime, and other structures. In order to maintain stiffness and strength with light weight, new design techniques must be employed when utilising these materials. Their stability, ultimate strength and loading capacity are the key issues pertaining to these engineering structures which have attracted a number of investigators to undertake in- depth research, either in an academic or actual engineering context. This paper presents a review of the optimisation techniques applied to buckling and post-buckling of stiffened panels. Papers published in the period from 2000 to May 2015 have been taken into consideration. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimisation formulations, optimisation algorithms and models available. Finally a critical discussion, giving some practical advice and pointing out and post-buckling of stiffened panels, is provided.

Improved Simulation Efficiency for Optimising Inflow Control Device Completions Using Suitably Upscaled Models and Parallel Global Search Optimisation Techniques

Inflow Control Device (ICD) completions can improve well performance by adjusting the inflow profile along the well and reducing the influx of unwanted fluids. The ultimate aim of using ICD completions is to provide maximum oil recovery and/or Net Present Value (NPV) over the life of the field. Proactive ICD optimisation studies use complex reservoir models and high-dimensional nonlinear objective functions to find the optimum ICD configurations over the life of the field. These complex models are generated from fine scale detailed geological models to accurately capture fluid flow behaviour in the reservoir. Although these high-resolution geological models can provide better performance predictions, their simulation runtimes can be computationally expensive and time consuming for performing proactive ICD optimisation studies that often require thousands of simulation runs. We propose a new workflow where we use upscaled and locally refined models coupled with parallelised global search optimisation techniques to improve the simulation efficiency when performing ICD optimisation and decision-making studies. Our approach preserves the flow behaviour in the reservoir and maintains the interaction between the reservoir and the well in the near wellbore region. Moreover, when coupled with parallel optimisation techniques, the simulation time is significantly reduced. We present an in-house code that couples global search optimisation algorithms (Genetic Algorithm and Surrogate Algorithm) with a commercial reservoir simulator to drive the ICD configurations. We evaluate the NPV as the objective function to determine the optimum ICD configurations. We apply and benchmark our approach to two different reservoir models to compare and analyse its efficiency and the optimisation results. Our analysis shows that our proposed approach reduces the run time by more than 80% when using the upscaled models and the parallel optimisation techniques. These results were based on a standard dual-core parallel desktop configuration. Additional results also showed further reduction in run time is possible when employing more processors. Additionally, when testing different ICD completion strategies (ICDs in producers only, ICDs in injectors only, and ICDs in both producers and injectors), the NPV can be increased by 9.6% for the optimised ICD completions. The novelty of our work is rooted in the much-improved simulation efficiency and better performance predictions that supports ICD optimisation and decision-making studies during field development planning to maximize profit and minimize risk over the life of the field.

Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

Topology Optimisation for Compliant Hip Implant Design and Reduced Strain Shielding

Stiff total hip arthroplasty implants can lead to strain shielding, bone loss and complex revision surgery. The aim of this study was to develop topology optimisation techniques for more compliant hip implant design. The Solid Isotropic Material with Penalisation (SIMP) method was adapted, and two hip stems were designed and additive manufactured: (1) a stem based on a stochastic porous structure, and (2) a selectively hollowed approach. Finite element analyses and experimental measurements were conducted to measure stem stiffness and predict the reduction in stress shielding. The selectively hollowed implant increased peri-implanted femur surface strains by up to 25 percentage points compared to a solid implant without compromising predicted strength. Despite the stark differences in design, the experimentally measured stiffness results were near identical for the two optimised stems, with 39% and 40% reductions in the equivalent stiffness for the porous and selectively hollowed implants, respectively, compared to the solid implant. The selectively hollowed implant’s internal structure had a striking resemblance to the trabecular bone structures found in the femur, hinting at intrinsic congruency between nature’s design process and topology optimisation. The developed topology optimisation process enables compliant hip implant design for more natural load transfer, reduced strain shielding and improved implant survivorship.