A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment

214

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

214

“…In the civil and infrastructure engineering domain, researchers have adopted this technique to detect cracks in infrastructure (Jiang & Adeli, ; Zhang et al., ), manage and automatize construction projects (Adeli, ; Fang et al., ; Ghosh‐Dastidar & Adeli, ; Luo et al., ), investigate highway safety (Chang, ; Pande & Abdel‐Aty, ), analyze traffic and transportation network (Nabian & Meidani, ; Yao et al., ), predict earthquakes (Less & Adeli, ; Panakkat & Adeli, ), evaluate structural reliability (Dai & Cao, ), and so on.…”

Section: Methodsmentioning

confidence: 99%

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

Chen

Leng

Labi

2019

105

Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in‐service roads, AV‐dedicated road networks, and AV test tracks. However, recent AV‐related fatalities on in‐service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real‐world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network–long short‐term memory (CNN–LSTM) algorithm for self‐driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving‐environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.

“…Deep learning takes advantage of a large‐scale database and allows computational models that are composed of multiple processing layers to learn representations of data (LeCun, Bengio, & Hinton, ). Owing to this excellent capability, various data forms have been exploited for deep learning‐based structural health monitoring (Cha, Choi, & Büyüköztürk, ; Dai & Cao, ; Rafiei & Adeli, , ). Particularly, in the area of image processing using deep learning techniques, one of the most important advancements in recent years is the development of convolutional neural networks (CNNs; Krizhevsky, Sutskever, & Hinton, ).…”

Section: Introductionmentioning

confidence: 99%

Concrete bridge surface damage detection using a single‐stage detector

Zhang

Chang

Jamshidi

2019

236

Early and timely detection of surface damages is important for maintaining the functionality, reliability, and safety of concrete bridges. Recent advancement in convolution neural network has enabled the development of deep learning‐based visual inspection techniques for detecting multiple structural damages. However, most deep learning‐based techniques are built on two‐stage, proposal‐driven detectors using less complex image data, which could be restricted for practical applications and possible integration within intelligent autonomous inspection systems. In this study, a faster, simpler single‐stage detector is proposed based on a real‐time object detection technique, You Only Look Once (YOLOv3), for detecting multiple concrete bridge damages. A field inspection images dataset labeled with four types of concrete damages (crack, pop‐out, spalling, and exposed rebar) is used for training and testing of YOLOv3. To enhance the detection accuracy, the original YOLOv3 is further improved by introducing a novel transfer learning method with fully pretrained weights from a geometrically similar dataset. Batch renormalization and focal loss are also incorporated to increase the accuracy. Testing results show that the improved YOLOv3 has a detection accuracy of up to 80% and 47% at the Intersection‐over‐Union (IoU) metrics of 0.5 and 0.75, respectively. It outperforms the original YOLOv3 and the two‐stage detector Faster Region‐based Convolutional Neural Network (Faster R‐CNN) with ResNet‐101, especially for the IoU metric of 0.75.