Collapse Warning System Using LSTM Neural Networks for Construction Disaster Prevention in Extreme Wind Weather

Wei, Chih-Chiang

doi:10.3846/jcem.2021.14649

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…In construction process field, 20 out of 61 observations were made regarding the construction of buildings, dams, roads, and tunnels. Within this field, the 60 out of 202 observations covered topics such as construction delays [22], crane, drilling and excavation tasks [14,18,24,39,48,70,74]; geological conditions [54], scaffolding collapse [68]; transport delays [31]; tunnelling [28,36,37,41,43,55,67]; workers and machinery location [34,40]. According to Erzaij et al [22], project suspensions are among the most persistent tasks facing the construction sector, due to the difficulty of the industry and the essential interdependence between the bases of delay risk.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…A location algorithm based on signal strength (RSS) and an artificial neural network (ann) was used for location analysis and risk assessment. Wei [68], developed wind speed prediction models based on various deep learning and machine learning techniques, in particular deep neural networks, neural networks with short-term memory, support vector regressions, random forest, and k-nearest neighbours. Subsequently, the author analysed the wind force on the scaffold and assessed the probability of the scaffold collapsing under the action of the wind.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

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A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

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Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

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show abstract

“…Han, et al [6] memprediksi data cuaca dari stasiun Harvard Graduate School of Design (GSD) menggunakan algoritma RNN dengan tingkat akurasi yang cukup tinggi dibandingkan dengan model seperti BNN dan NN. Wei [7] menggunakan beberapa model untuk memprediksi cuaca yakni long short-term memory neural networks (LSTM-NN), deep neural networks (DNN), random forest (RF), support vector regressions (SVR) dan k-nearest neighbors (K-NN). Andini dan Utomo [8] juga memprediksi data iklim kota Bandung Jawa Barat dengan menggunakan kombinasi RNN dan Long Short Term Memory (LSTM).…”

Section: Pendahuluanunclassified

“…Adapun kombinasi persamaan (7) dengan persamaan (8) yakni gabungan pola data musiman dan tidak musiman akan menghasilkan model multiplikatif musiman ARIMA (SARIMA) (p, d, q)(P, D, Q) s [22] yang ditampilkan pada persamaan (9):…”

Section: Melakukan Optimasi Model Rnn Dengan Metode Adamunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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Extreme weather modeling is a challenge for modeling experts in Indonesia and the world. Extreme weather prediction is a complex problem because the chances of it happening are very small, so the developed models often have a low level of accuracy. The purpose of this research is to combine the classic model, Autoregressive Integrated Moving Average (ARIMA), recurrent neural network (RNN) model using Adam and SGD estimation (RNN-Adam and RNN-SGD) with the reLU, tanh, sigmoid and gaussian activation functions. In addition, the ARIMA-RNN mix model was also demonstrated in this study. These models are applied to monthly period extreme weather data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of West Sulawesi Province which are converted into training data and test data. The RMSE value is used to see the level of prediction accuracy in both training data and test data. Based on the research results, the best model obtained for modeling Indonesia’s extreme weather is the ARIMA-RNN-Adam mix model with the reLU activation function based on the RMSE value on the training and test data. At n = 50, the smallest RMSE and MSE values of the third model are the ARIMA-RNN-Adam model which is 0.23212 using the reLU activation function, then the ARIMA-RNN-SGD model which is 0.25432 with the same activation function, while the ARIMA value is 0.3270. At n=100 it can be seen that the smallest RMSE and MSE values of the three models are the ARIMA-RNN-Adam model which is equal to 0.25149 using the reLU activation function, then the ARIMA-RNN-SGD model which is equal to 0.25256 with the same activation function, while the ARIMA value is 0.2644.

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“…Tsunamis [1-3], strong winds [4], earthquakes [5] and fire [6] disasters are the usual risks considered by designers and engineers to maintain the safety and reliability of building structures and underground structures. Due to the impact of tsunamis, the self-weight of the structure is insufficient to withstand the static and dynamic forces exerted by the water on them [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Up-Floating Destruction and Reinforcement Measures of Damaged Basement Based on the Bending Moment and Deformation Analysis

Fan

et al. 2023

Buildings

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Based on the up-floating incident of the basement in a high-rise residential building, the finite element (FE) model of the up-floating destruction region is established to investigate the damage mechanism. The stress states and the deformations of the basement structure are obtained under complex loads including water buoyancy forces, vehicle loads and construction loads. To assess the extent of damage, a novel damage indicator is defined based on two levels: the cracking bending moments and the yield bending moment. The first-level cracking bending moment, second-level cracking bending moment and the yield bending moment can be determined using the section stratification method. By comparing the maximum bending moment of the component with its corresponding cracking moment, one can determine whether the cracks have occurred and assess their severity. Meanwhile, the antifloating failure model is constructed to analyze the mechanism of the up-floating destruction. Finally, a detailed reinforcement treatment plan of ‘decompression first and then reinforcement’ is presented to reinforce and repair the damaged basement structure. The mechanism analysis of the up-floating destruction and the comprehensive reinforcement treatments ensure the simulation of the life cycle of emergence, development and treatment to ensure structural safety.

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Collapse Warning System Using LSTM Neural Networks for Construction Disaster Prevention in Extreme Wind Weather

Cited by 10 publications

References 52 publications

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Up-Floating Destruction and Reinforcement Measures of Damaged Basement Based on the Bending Moment and Deformation Analysis

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