In shear wall building design, the initial process requires the interaction between the architectural and structural engineering groups to define the adequate wall layout, usually done with a trial-and-error procedure to fulfill architectural and engineering needs, slowing down the design process. For the engineering analysis, first, the wall thickness and length are required to check the building deformation limits, base shear strength, among other parameters. For this reason, the present investigation develops a structural design platform for reinforced concrete wall buildings that uses a deep neural network to predict the wall’s thickness and length based on previous architectural and engineering projects. The study includes, in the first place, the surveying of the architectural and engineering plans for a total of 165 buildings constructed in Chile; the generated database has the geometric and topological definition of the walls and the slabs. As a second stage, a model was trained for the regression of the wall segments’ thickness and length, making use of a feature vector that models the variation between the architectural and the engineering plans for a set of conditions such as the thickness, connectivity (vertical and horizontal), area, wall density, the distance between elements, wall angles, foundation soil type, among other engineering parameters. The regression model results in terms of R²-value are 0.995 and 0.994 for the predicted wall thickness and length, respectively, proving to be a reliable method for the initial engineering wall definition.

在剪力墙建筑设计中，初始过程需要建筑和结构工程组之间的相互作用来定义适当的墙体布局，通常通过反复试验来满足建筑和工程需要，从而减慢了设计过程。为了进行工程分析，首先，需要墙的厚度和长度来检查建筑物的变形极限，基础抗剪强度以及其他参数。因此，本研究开发了一种用于钢筋混凝土墙建筑的结构设计平台，该平台使用深度神经网络根据以前的建筑和工程项目来预测墙的厚度和长度。该研究首先包括 对智利总共建造的165座建筑物的建筑和工程计划进行了调查；生成的数据库具有墙和板的几何和拓扑定义。第二阶段，使用特征向量对壁段的厚度和长度进行回归的模型训练，该特征向量可针对一系列条件（例如厚度，连通性）对建筑和工程计划之间的变化进行建模。 （垂直和水平），面积，墙体密度，单元之间的距离，墙体角度，地基类型以及其他工程参数。回归模型的结果以R表示 使用特征向量对模型进行训练，以分析壁段的厚度和长度，以针对一组条件（例如厚度，连通性（垂直和水平））对建筑和工程计划之间的变化进行建模，面积，墙体密度，元素之间的距离，墙体角度，地基土壤类型以及其他工程参数。回归模型的结果以R表示 使用特征向量对模型进行训练，以分析壁段的厚度和长度，以针对一组条件（例如厚度，连通性（垂直和水平））对建筑和工程计划之间的变化进行建模，面积，墙体密度，元素之间的距离，墙体角度，地基土壤类型以及其他工程参数。回归模型的结果以R表示预测的壁厚和长度的²值分别为0.995和0.994，被证明是初始工程壁定义的可靠方法。