Structural design of reinforced concrete buildings based on deep neural networks

Abstract 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.

[1]  Leonardo M. Massone,et al.  Use of convolutional networks in the conceptual structural design of shear wall buildings layout , 2021, Engineering Structures.

[2]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[3]  H. Tran-Ngoc,et al.  An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm , 2019, Engineering Structures.

[4]  Ye Xia,et al.  Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection , 2020 .

[5]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[6]  R. Boroschek,et al.  Title : Seismic Performance of High-rise Concrete Buildings in Chile , 2022 .

[7]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[8]  Jie Chen,et al.  Decision-Making Algorithm for Multisensor Fusion Based on Grey Relation and DS Evidence Theory , 2016, J. Sensors.

[9]  Leonardo M. Massone,et al.  Fundamental principles of the reinforced concrete design code changes in Chile following the Mw 8.8 earthquake in 2010 , 2013 .

[10]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[11]  R. Boroschek,et al.  The quest for resilience: The Chilean practice of seismic design for reinforced concrete buildings , 2020 .

[12]  Qi Tian,et al.  Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data , 2019, ArXiv.

[13]  Wael W. El-Dakhakhni,et al.  Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls , 2019 .

[14]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[15]  Tan Liu,et al.  A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications , 2016, J. Sensors.

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[18]  Mohamed A. Shahin,et al.  State-of-the-art review of some artificial intelligence applications in pile foundations , 2016 .

[19]  Reginald DesRoches,et al.  The promise of implementing machine learning in earthquake engineering: A state-of-the-art review , 2020, Earthquake Spectra.

[20]  Jie Chen,et al.  The Improvement of DS Evidence Theory and Its Application in IR/MMW Target Recognition , 2015, J. Sensors.

[21]  Jack P. Moehle,et al.  Seismic Design and Construction Practices for RC Structural Wall Buildings , 2012 .

[22]  X. W. Ye,et al.  A review on deep learning-based structural health monitoring of civil infrastructures , 2019 .

[23]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[24]  L. Paul Chew,et al.  Constrained Delaunay triangulations , 1987, SCG '87.

[25]  Yu Zhang,et al.  Shear wall layout optimization for conceptual design of tall buildings , 2017 .

[26]  Two simple algorithms for constructing a two-dimensional constrained Delaunay triangulation , 1993 .