Search

The Aerial Building Machine (ABM) is a complex construction equipment used in high-rise building construction, facing challenges due to environmental uncertainties. This paper introduces a multi-condition generative design framework to improve ABM's visualization, operability, and intelligence. It seamlessly integrates real-time data between geometric and physical models and employs an ensemble deep learning model for objective value prediction, using a snapshot strategy. Combining structural reliability concepts with Latin hypercube sampling-based stochastic optimization, an optimal design scheme is obtained for uncertain loads. An ABM case study in China illustrates the approach's feasibility, showing it meets reliability requirements across different conditions and achieves significant improvements (16.59% under normal conditions and 16.91% under extreme wind conditions). Additionally, ensemble deep learning outperforms existing methods for ABM structural reliability estimation. Identifying optimal designs and evaluation options, this paper contributes a multi-condition optimization approach for enhanced structural reliability and establishes an efficient generative design workflow and system for exploring a vast solution space. • A multi-condition generative design framework for ABM is proposed. • The ensemble deep learning approach is employed to predict the structural response precisely. • The load uncertainties are considered according to the developed LHS-based SO approach. • A real aerial building machine in China is taken as a case study for illustration. • The proposed approach shows great performance in the structural optimization design. [ABSTRACT FROM AUTHOR]

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy ("FacalCE") is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and "FacalCE" contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds. • A deep learning method named DAPCNet is developed for 3D point cloud segmentation. • An improved 3D dual attention module is introduced to enhance model performance. • A custom loss "FacalCE" is designed to strengthen feature learning with handling data imbalance. • Conducting comparisons to examine the model performance for segmentation. • The developed method demonstrates outstanding performance, achieving an MIoU score of 0.8597 when applied to tunnel point clouds. [ABSTRACT FROM AUTHOR]

Laser scanning is used as a modern means to capture data from tunnels to assess their condition, but automated processing requires robust component detection and deterioration characterization. In order to segment 3D tunnel point clouds aiming at more accurate results with high time efficiency, this paper describes a point cloud technique that collects actual tunnel scenes and develops an attention-enhanced sampling point cloud network named ASPCNet. In the developed model, the feature embedding module is responsible to process the point cloud data for local features followed by the attention module for enhancing the feature extraction and learning. Additionally, the point downsampling-upsampling structure fully assists the model to strengthen the capability to process point clouds for time efficiency. In the training process, a weighted focal loss is designed to enhance the model learning by eliminating the effect of data imbalance. The developed ASPCNet is trained and then tested on a dataset collected from a cross-river metro tunnel section in China, demonstrating its efficiency and effectiveness. In comparison with different sampling ratios, state-of-the-art methods, and sampling methods, the ASPCNet with a uniform sampling rate of 2 exhibits the best performance, achieving an overall accuracy of 0.9758, a mean Intersection over Union (MIoU) of 0.8988, and an inference time of 4.1 s, demonstrating that the sampling structure involved in this research boosts the time efficiency, the developed model has superior performance, and the sampling method adopted is beneficial to strengthen the model performance. • A deep learning method named ASPCNet is developed for 3D point cloud segmentation. • It consists of several modules to process point clouds for accuracy and time efficiency. • A focal loss is designed to handle data imbalance and boost feature learning. • Effectiveness and efficiency are verified on a dataset with six classes of tunnel point clouds. • The developed method performs excellently with high accuracy and great efficiency. [ABSTRACT FROM AUTHOR]

In the desert region of northwest China, the frequency of wind-sand disasters is high. All types of concrete buildings built in this area face severe wind erosion due to high wind speed, resulting in varying degrees of wind-erosion damage to concrete. To accomplish intelligent identification of concrete wind-erosion damage, a concrete wind erosion experiment was conducted in the laboratory, and a concrete wind-erosion damage dataset was generated under the interference of water stains, scratches, shooting distance, and background noise. This paper combined with transformer theory to improve YOLO-v4 and proposed an object detection algorithm called MHSA-YOLOv4 suitable for wind-erosion damage of concrete. The results demonstrate that MHSA-YOLOv4 exhibits improved object detection performance than YOLO-v3, improved YOLO-v3, and YOLO-v4. On the test set, ACC, Precision, Recall, and mAP of MHSA-YOLOv4 are 91.30%, 91.52%, 92.31%, and 0.89, respectively. MHSA-YOLOv4 can accurately identify wind-erosion damage of concrete images under different test conditions, which reflects strong robustness. The applicability of computer vision technology to the intelligent identification of wind-erosion damage on concrete has been verified. • The dataset of wind-erosion of concrete was established to simulate the spalling of concrete. • A object detection algorithm called MHSA-YOLOv4 was proposed based on Transformer. • MHSA-YOLOv4 can detect concrete wind-erosion damage with high-precision. [ABSTRACT FROM AUTHOR]

Traditional construction progress tracking relies on labor-intensive activities with time lags, potential man-made errors, and inefficient progress management, which demands for an innovative and automated progress tracking approach. This paper describes a deep learning method that utilizes image segmentation to automatically evaluate the wall construction progress of an entire floor with the progress results streamlined to BIM. The approach was applied to a case study in China for assessing plastering construction activities with high segmentation accuracy (mean average precision = 96.8%). Further improvement of Mask Region-Based Convolutional Neural Networks (Mask R-CNN) and evaluation of its superiority over other models have also been discussed. This study provides both theoretical and practical references for unmanned supervision of progress tracking and intelligent schedule management. • Described an evaluation framework to recognize wall progress of an entire floor. • Proposed an image segmentation method to calculate wall construction area. • Improved Mask R-CNN algorithm to increase segmentation accuracy. • Discussed the influence of image light on segmentation accuracy. • Applied the framework to a case study with mean Average Precision of 96.8%. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]