Infrastructure BIM Platform for Lifecycle Management

Recently, the application of the BIM technique to infrastructure lifecycle management has increased rapidly to improve the efficiency of infrastructure management systems. Research on the lifecycle management of infrastructure, from planning and design to construction and management, has been carried out. Therefore, a systematic review of the literature on recent research is performed to analyze the current state of the BIM technique. State-of-the-art techniques for infrastructure lifecycle management, such as unmanned robots, sensors and processing techniques, artificial intelligence, etc., are also reviewed. An infrastructure BIM platform framework composed of BIM and state-of-the-art techniques is then proposed. The proposed platform is a web-based platform that contains quantity, schedule (4D), and cost (5D) construction management, and the monitoring systems enable collaboration with stakeholders in a Common Data Environment (CDE). The lifecycle management methodology, after infrastructure construction, is then completed and is developed using state-of-the-art techniques using unmanned robots, scan-to-BIM, and deep learning networks, etc. It is confirmed that collaboration with stakeholders in the CDE in construction management is possible using an infrastructure BIM platform. Moreover, lifecycle management of infrastructure is possible by systematic management, such as time history analysis, damage growth prediction, decision of repair and demolition, etc., using a regular inspection database based on an infrastructure BIM platform.

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Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods

Applied Sciences ◽

10.3390/app10186210 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6210

Author(s):

Ruihao Zheng ◽

Chen Xiong ◽

Xiangbin Deng ◽

Qiangsheng Li ◽

Yi Li

Keyword(s):

Neural Network ◽

Machine Learning ◽

Ground Motions ◽

Low Frequency ◽

Time History ◽

Time History Analysis ◽

Machine Learning Algorithms ◽

Learning Networks ◽

History Analysis ◽

Deep Layers

This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R2 = 0.8737) compared with that of the BPNN (R2 = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.

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PERILAKU STRUKTUR BAJA TIPE MRF DENGAN BEBAN LATERAL BERDASARKAN SNI 1726-2012 DAN METODE PERFORMANCE BASED PLASTIC DESIGN (PBPD)

Jurnal Teknik Sipil ◽

10.24002/jts.v13i1.640 ◽

2014 ◽

Vol 13 (1) ◽

Author(s):

Nidiasari Jati Sunaryati Eem Ikhsan

Keyword(s):

Pushover Analysis ◽

Time History ◽

Time History Analysis ◽

Plastic Design ◽

History Analysis ◽

Non Linear ◽

Static Pushover Analysis

Struktur rangka baja pemikul momen merupakan jenis struktur baja tahan gempa yang populer digunakan. Daktilitas struktur yang tinggi merupakan salah satu keunggulan struktur ini, sehingga mampu menahan deformasi inelastik yang besar. Dalam desain, penggunaan metode desain elastis berupa evaluasi non-linear static (Pushover analysis) maupun evaluasi non-linear analisis (Time History Analysis) masih digunakan sebagai dasar perencanaan meskipun perilaku struktur sebenarnya saat kondisi inelastik tidak dapat digambarkan dengan baik. Metode Performance-Based Plastic Design (PBPD) berkembang untuk melihat perilaku struktur sebenarnya dengan cara menetapkan terlebih dahulu simpangan dan mekanisme leleh struktur sehingga gaya geser dasar yang digunakan adalah sama dengan usaha yang dibutuhkan untuk mendorong struktur hingga tercapai simpangan yang telah direncanakan. Studi dilakukan terhadap struktur baja 5 lantai yang diberi beban gempa berdasarkan SNI 1726, 2012 dan berdasarkan metode PBPD. Hasil analisa menunjukkan bahwa struktur yang diberi gaya gempa berdasarkan metode PBPD mencapai simpangan maksimum sesuai simpangan rencana dan kinerja struktur yang dihasilkan lebih baik .

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Response Law and Influencing Factors of the Soil under Action of Pile Driving Vibration Based on Midas/GTS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.979 ◽

2013 ◽

Vol 353-356 ◽

pp. 979-983

Author(s):

Dong Zhang ◽

Jing Bo Su ◽

Hui De Zhao ◽

Hai Yan Wang

Keyword(s):

Damping Ratio ◽

Large Diameter ◽

Time History ◽

Pile Driving ◽

Analysis Model ◽

Vibration Load ◽

History Analysis ◽

Study Results ◽

Propagation Law ◽

Dynamic Time

Due to the upgrade and reconstruct of a high-piled wharf, the piling construction may cause the damage of the large diameter underground pipe of a power plant nearby. For this problem, a dynamic time-history analysis model was established using MIDAS/GTS program. Based on the analysis of the pile driving vibration and its propagation law, some parameters, such as the modulus of the soil, the Poissons ratio of soil, the action time of vibration load and the damping ratio of the soil that may have an effect on the response law of the soil, were studied. The study results not only serve as an important inference to the construction of this case, but also accumulate experience and data for other similar engineering practices.

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Gamma-ray Spectrometry in Geothermal Exploration: State of the Art Techniques

Energies ◽

10.3390/en7084757 ◽

2014 ◽

Vol 7 (8) ◽

pp. 4757-4780 ◽

Cited By ~ 17

Author(s):

Alistair McCay ◽

Thomas Harley ◽

Paul Younger ◽

David Sanderson ◽

Alan Cresswell

Keyword(s):

Gamma Ray ◽

State Of The Art ◽

Gamma Ray Spectrometry ◽

Geothermal Exploration ◽

Art Techniques

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Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition

Sensors ◽

10.3390/s21124233 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4233

Author(s):

Bogdan Mocanu ◽

Ruxandra Tapu ◽

Titus Zaharia

Keyword(s):

Emotion Recognition ◽

Loss Function ◽

State Of The Art ◽

Disease Diagnosis ◽

Data Representation ◽

Speech Emotion Recognition ◽

Audio Features ◽

Global Accuracy ◽

Space Data ◽

Art Techniques

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human–machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.

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Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

IEEE Access ◽

10.1109/access.2021.3107316 ◽

2021 ◽

Vol 9 ◽

pp. 118584-118605

Author(s):

Munyaradzi Munochiveyi ◽

Arjun Chakravarthi Pogaku ◽

Dinh-Thuan Do ◽

Anh-Tu Le ◽

Miroslav Voznak ◽

...

Keyword(s):

State Of The Art ◽

Open Issues ◽

Art Techniques

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Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation

Diagnostics ◽

10.3390/diagnostics11060967 ◽

2021 ◽

Vol 11 (6) ◽

pp. 967

Author(s):

Amirreza Mahbod ◽

Gerald Schaefer ◽

Christine Löw ◽

Georg Dorffner ◽

Rupert Ecker ◽

...

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Computer Assisted ◽

Important Criterion ◽

Histological Image ◽

Computer Mediated ◽

Image Patches ◽

Processing Techniques ◽

The Impact ◽

Instance Segmentation

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.

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Record-to-record variability and code-compatible seismic safety-checking with limited number of records

Bulletin of Earthquake Engineering ◽

10.1007/s10518-020-01024-6 ◽

2021 ◽

Author(s):

Fatemeh Jalayer ◽

Hossein Ebrahimian ◽

Andrea Miano

Keyword(s):

Seismic Hazard ◽

Linear Time ◽

Time History ◽

Time History Analysis ◽

Seismic Safety ◽

Hazard Curve ◽

History Analysis ◽

Non Linear ◽

Seismic Hazard Curve ◽

Cloud Analysis

AbstractThe Italian code requires spectrum compatibility with mean spectrum for a suite of accelerograms selected for time-history analysis. Although these requirements define minimum acceptability criteria, it is likely that code-based non-linear dynamic analysis is going to be done based on limited number of records. Performance-based safety-checking provides formal basis for addressing the record-to-record variability and the epistemic uncertainties due to limited number of records and in the estimation of the seismic hazard curve. “Cloud Analysis” is a non-linear time-history analysis procedure that employs the structural response to un-scaled ground motion records and can be directly implemented in performance-based safety-checking. This paper interprets the code-based provisions in a performance-based key and applies further restrictions to spectrum-compatible record selection aiming to implement Cloud Analysis. It is shown that, by multiplying a closed-form coefficient, code-based safety ratio could be transformed into simplified performance-based safety ratio. It is shown that, as a proof of concept, if the partial safety factors in the code are set to unity, this coefficient is going to be on average slightly larger than unity. The paper provides the basis for propagating the epistemic uncertainties due to limited sample size and in the seismic hazard curve to the performance-based safety ratio both in a rigorous and simplified manner. If epistemic uncertainties are considered, the average code-based safety checking could end up being unconservative with respect to performance-based procedures when the number of records is small. However, it is shown that performance-based safety checking is possible with no extra structural analyses.

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