标题: 类神经网路机械学习模式发展:房屋结构之初步设计
Development of Neural Network Machine Learning Models:Preliminary Design in Building Structure
作者: 詹君治
Jing-Chi Jan
洪士林
Shih-Lin Hung
土木工程学系
关键字: 人工智慧;结构设计;类神经网路;机械学习;房屋结构;Artificial Intelligence;Structural Design;Artificial Neural Network;Machine Learning;Building Structure
公开日期: 1999
摘要: 电脑由于计算效率高,因此工程师常常使用它处理高计算量的问题。随着相关电脑技术的进步,工程师开始期待电脑能更佳有效的辅助设计。基于朝向自动化设计的远程目标,人工智慧技术正逐渐为土木工程人士所重视,并且期望电脑能处理经验导向的问题。现阶段的人工智慧技术虽尚不足以完全模拟工程师的逻辑思考方式,但是将人工智慧应用于结构设计中已证实能获得相当程度效率提升。
过去的文献可得知,倒传递类神经网路(Back-propagation Neural Network)为土木工程中最常被讨论到的机械学习模式,其优点为演算法简单且具有良好的一般化能力。然而其学习策略是采取数学最佳化的方式强记经验,因此局部最小化成为无法避免的问题,尤其是对于需要大量案例的复杂问题,想达到良好学习收敛的困难度相形提高。除此之外,耗时的学习过程也是造成业界不愿尝试的理由之一。
因此本篇论文的主旨是要发展全新的机械学习模式,以期能让人工智慧技术在结构设计过程中发挥更大的功效。在本论文将重心放在房屋结构设计中的初步设计阶段,发展了两个完全不同架构的机械学习模式:(1)IFN (Integrated Fuzzy Neural Network)类神经网路;(2)MS_CMAC (Macro Structure CMAC)类神经网路。IFN由主网路-UFN (Unsupervised Fuzzy Neural Network)推理模式和辅助网路-监督式类神经网路所组成,其中UFN推理模式采用局部资讯来推理,因此随着搜集到的案例增加其预测准确性将不断的提升。除此之外模式本身具有自我调整系统参数的能力,可随着案例库的资料异动自动去修正参数达到最佳的预测能力。CMAC为一种极为快速的监督式类神经网路,MS_CMAC则是串连了许多单自由度的CMAC成树状结构,然后再以time inversion技巧执行计算的工作。其概念上近似于将多自由度的问题切割成一群单自由度的子问题,因此降低了神经网路学习收敛的困难度,间接得也获得较高精确的预测结果。
为了验证IFN和MS_CMAC的能力, IFN将应用于初始设计问题和钢结构粱的设计问题,前者是一极为复杂的经验导向问题,后者则为需要反覆计算的工作。MS_CMAC则将应用于评估钢结构设计中所需要用到的设计参数,复杂的数值计算解析过程将由类神经网路所取代。论文中的结果将证实,IFN和MS_CMAC确实优于已被广泛使用的BPN。
Computer programs are widely used to assist engineers in solving problems by shifting the burden of numerical computation to the machine. Furthermore, new methods and tools encourage civil engineers to use numerical computation in creative and imaginative ways. Despite the completely automatic structural design is presently not available; the efficiency of the conventional structural design is significantly improved by adopting some techniques of artificial intelligence (AI).
Applying neural network computing, one of the artificial intelligence techniques, to structural engineering is currently an active subject in computer-aided design. Most of the previous researches concentrated on the back-propagation neural network (BPN) because BPN has a good generalization. However, the BPN sometime performs poor learning convergence when a large number of instances are used for a complicated problem, owing to its global optimization learning scheme. In addition, a long computational time in learning stage is another drawback for engineers to use BPN in structural design.
The goal of this dissertation is to develop novel machine learning models to make structural design system more powerful, especially for preliminary design in building structural design. Based on the information flow in building structural design, two different kind of neural networks, integrated fuzzy neural network (IFN) and macro structure CMAC (MS_CMAC), are developed. (1) The IFN learning model combined an unsupervised fuzzy neural network (UFN) reasoning model with a supervised neural network as an assistant network. The UFN reasoning model adopts local information scheme to interpret a large number of instances for complicated problems within an acceptable computing time. Meanwhile, a self-organized learning is developed to refine the UFN reasoning. (2) The CMAC is a supervised learning model used mainly in control due to its rapid learning. The MS_CMAC is a tree-based structure of one-dimensional CMACs, where the ensemble is trained by the time inversion technique. The main feature of MS_CMAC is to decompose a multi-dimensional problem into a set of one-dimensional sub-problems so as to improve the learning convergence and prediction.
For verifying the feasibility of IFN and MS_CMAC in structural design, the IFN learning model is utilized to model the initial design of building structure and steel beam design problems. The initial design is an experience-oriented problem, and the steel beam design is an iterative process under LRFD specification. Also, the MS_CMAC is employed to determine some design coefficients which are generally obtained by numerical approaches. The results indicate that the two neural networks are useful tools for engineers to solve structural design problems.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT880015005
http://hdl.handle.net/11536/65102
显示于类别:Thesis