Multi-objective Optimisation for Sustainable Steel Structures Employing Artificial Intelligence

目的本研究项目中提出的方法的力量可以用于进行一系列以大跨度结构系统为重点的研究，如考古遗址、机场航站楼、音乐厅和火车/汽车站的屋顶。这样的结构形式对造型构成了特殊的挑战：它们通常有形状不同寻常的大型开放空间，内部柱很少，因此它们依赖三角形空间桁架和框架系统协同工作来支撑建筑的荷载。在设计过程的早期使用计算机模拟--当建筑的份额确定时--也会对体现能量产生重大影响(生命周期分析-LCA研究)。精心选择结构的几何形状和布局可以减少内力，减少支撑所需的能源密集型结构材料的量。准确预测特定大跨度构件的使用寿命对于及时、经济地采取适当措施至关重要。然而，传统的预测设计模型依赖于对通常使用的构件(标准尺寸)的简化假设，这往往导致不准确的估计。虽然数据驱动方法目前主要用于提高性能预测，但它们仍然依赖于经验公式，具有许多局限性。该项目将采用最近为结构工程应用开发的人工智能(AI)方法，这被证明是经典建模技术的一种有效替代方法，并试图减少结果的不确定百分比，并节省大量人类在实验中花费的时间和精力。方法本研究将专注于寻找一种有效的方案来优化形状和构件刚度分布，以创建具有比仅使用经验方法或甚至仅使用拓扑优化技术(最近由实践工程师采用)创建的具有更高屈曲强度的大跨度钢构件(梁)。几何和材料特性将针对目标进行优化，以最大限度地提高静态和动态(振动)作用下的线性屈曲载荷。我们将首次使用Altair的HyperWorks软件工具的高级算法在这种规模上研究屈曲优化。结合机器(监督)学习神经网络算法(通过回归分析)，将演示经典预测模型的局限性。利用有限元软件进行参数非线性有限元分析，为机器学习算法提供有效的数据。此外，将对制造结构材料和部件所需的初始能量以及未来的运行能量进行量化和比较，以设计节能结构。影响本项目产生的知识可以以有趣和意想不到的方式推动解决方案，并通过设计高性能、创新性和建筑表现力的大跨度轻质和僵硬(无支撑)结构构件，产生新的建筑设计和法规(包括低层和高层轻质结构)。

AimThe power of the approach proposed in this research project can be exercised performing a series of studies focusing on long-span structural systems such as roofs for archaeological sites, airport terminals, concert halls, and train/bus stations. Such structural forms pose a special modelling challenge: they often have large open spaces with unusual shapes and few interior columns, so they rely on systems of triangular space trusses and frames working together to support the load of the building. The use of computer simulation early in the design process - when the share of the building in determined - can have a major impact on embodied energy (Life Cycle Analyses - LCA studies) as well. Careful choice of the geometry and layout of the structure can reduce internal forces and decrease the amount of energy-intensive structural materials required for support.Accurate service-life prediction of particular long-span members is vital for taking appropriate measures in a time- and cost-effective manner. However, the conventional prediction design models rely on simplified assumptions for typically used members (standard sizes) often leading to inaccurate estimations. Although data driven approaches mainly used today to enhance the performance prediction, they still depend on empirical formulas with many limitations. This project will engage with Artificial Intelligence (AI) methods recently developed for structural engineering applications, as is proving to be an efficient alternative approach to classic modelling techniques, and attempt to reduce the percentage of uncertainty of the results as well as saving significant human time and effort spent in experiments.MethodologyThis study will focus on finding an efficient scheme for the optimisation of both shape and member stiffness distributions in order to create long-span steel members (beams) with higher buckling strength than the one created by just using empirical approaches or even only topology optimisation techniques (recently adopted by practising engineers). Geometric and material characteristics will be optimised for a target to maximise linear buckling load under static as well as dynamic (vibration) actions. Buckling optimisation will be studied for first time at this scale using advanced algorithms of Altair's Hyperworks software tools. Together with machine (supervised) learning Neural Network algorithms (via regression analyses), the limitations of classical prediction models will be demonstrated. Parametric nonlinear finite element (FE) analyses will be performed using ANSYS software to feed the machine learning algorithm with validated data. In addition, both the initial energy required for making structural materials and components as well as the future operational energy will be quantified and compared for the design of energy-efficient structures. ImpactThe knowledge generated by this project can push solutions in interesting and unexpected ways and lead to new building designs and regulations (including low- and high- storey lightweight structures) via the design of long-span lightweight and stiff (support-less) structural members that are high-performance, innovative and architecturally expressive.