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Hamed Raoofi, Asa Sabahnia, Daniel Barbeau and Ali Motamedi
Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods ca...
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Hua Huang, Zhenfeng Peng, Jinkun Hou, Xudong Zheng, Yuxi Ding and Han Wu
Disc buckle steel pipe brackets are widely used in building construction due to the advantages of its simple structure, large-bearing capacity, rapid assembling and disassembling, and strong versatility. In complex construction projects, the uncertaintie...
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Mohammed Saïd Kasttet, Abdelouahid Lyhyaoui, Douae Zbakh, Adil Aramja and Abderazzek Kachkari
Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-...
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Chinyang Henry Tseng, Woei-Jiunn Tsaur and Yueh-Mao Shen
In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrus...
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Junling Zhang, Min Mei, Jun Wang, Guangpeng Shang, Xuefeng Hu, Jing Yan and Qian Fang
The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engine...
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Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attenti...
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Jiaqi Zhao, Ming Xu, Yunzhi Chen and Guoliang Xu
Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer?s experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to ...
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Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, Ould el Moctar, Thomas E. Schellin and Angelo Ciaramella
Two methods were compared to predict a ship?s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship?s...
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Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, Karoline Moenster, Thomas E. Schellin, Ould EL Moctar, Christoph Rasewsky and Angelo Ciaramella
The authors proposed a direct comparison between white- and black-box models to predict the engine brake power of a 15,000 TEU (twenty-foot equivalent unit) containership. A Simplified Naval Architecture Method (SNAM), based on limited operational data, ...
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Jihun Kim, Il Do Ha, Sookhee Kwon, Ikhoon Jang and Myung Hwan Na
Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to...
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