The Ba Lang sand beaches, located north of the Nha Trang Bay in Central Vietnam, are famous tourist attractions. However, they are experiencing shoreline and coastal erosion retreat, which is attributed to natural cau...The Ba Lang sand beaches, located north of the Nha Trang Bay in Central Vietnam, are famous tourist attractions. However, they are experiencing shoreline and coastal erosion retreat, which is attributed to natural causes (such as tropical depressions, storms, and monsoons) as well as human impacts (such as hydropower generation, sand dredging, and coastal works). According to the forecast of the Vietnam Ministry of Natural Resources and Environment, global climate change will cause the sea level to rise by 74 cm along the coast from the Dai Lanh Cape to the Ke Ga Cape (including the Ba Lang beaches) by the end of this century in the representative concentration pathway (RCP) 8.5 scenario. Sea level rise (SLR) due to global climate change is expected to aggravate the coastal erosion and shoreline retreat problems. In this study, coupled numerical models with the spectral wave module (MIKE 21 SW), hydrodynamic module (MIKE 21 HD), and sand transport module (MIKE 21 ST) in the MIKE 21 package were used to simulate waves, current fields, and sediment dynamics along the Ba Lang beaches considering the impact of SLR. These models were calibrated with the field data measured in December 2016. The results showed that SLR caused the wave height to increase and reduced the current speed and total sediment load in monsoon conditions. The increase in wave height was even intensified under the joint impact of SLR and extreme events.展开更多
The offshore renewable energy industry has been developing farms of floating offshore wind turbines in water depths up to 100 m.In Vietnam,floating offshore wind turbines have been developed to increase the production...The offshore renewable energy industry has been developing farms of floating offshore wind turbines in water depths up to 100 m.In Vietnam,floating offshore wind turbines have been developed to increase the production of clean and sustainable energy.The mooring system,which is used to keep the turbine stable and ensure the safety and economic efficiency of wind power production,is an important part of a floating offshore wind turbine.Appropriate selection of the mooring type and mooring line material can reduce the risks arising from the motion of wind turbines.Different types of mooring line material have been simulated and compared in order to determine the optimal type with the minimum motion risk for a floating wind turbine.This study focused on numerical modeling of semi-taut mooring systems using nonlinear materials for a semi-submersible wind turbine.Several modeling approaches common to current practice were applied.Hydrodynamic analysis was performed to investigate the motion of the response amplitude operators of the floating wind turbine.Dynamic analysis of mooring systems was performed using a time domain to obtain the tension responses of mooring lines under the ultimate limit states and fatigue limit states in Vietnamese sea conditions.The results showed that the use of nonlinear materials(polyester and/or nylon)for mooring systems can minimize the movement of the turbine and save costs.The use of synthetic fibers can reduce the maximum tension in mooring lines and the length of mooring lines.However,synthetic fiber ropes showed highly nonlinear load elongation properties,which were difficult to simulate using numerical software.The comparison of the characteristics of polyester and nylon mooring lines showed that the maximum and mean tensions of the nylon line were less than those of the polyester line.In addition,the un-stretched length of the polyester line was greater than that of the nylon line under the same mean tension load.Therefore,nylon material is recommended for the mooring lines of a floating offshore wind turbine.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’safety...Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’safety without interrupting their normal operations.This paper develops a novel framework using prompt engineering for seamlessly integrating users’domain knowledge about vibration signals with the advanced inference ability of well-trained large language models(LLMs)to accurately identify the actual states of structures.The proposed framework involves formulating collected data into a standardized form,utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals,and implementing an in-house program with the help of LLMs to perform damage detection.The advantages,as well as limitations,of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature,demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.展开更多
Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approx...Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.展开更多
The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation.The key idea is to design a deep learning archit...The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation.The key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series data.The proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future values.The viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel frame.Furthermore,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.展开更多
During the use of constructions, they will be degraded. Due to the negative impact on structures such as increase in vertical load, horizontal windy load needs to evaluate the current state of the constructions before...During the use of constructions, they will be degraded. Due to the negative impact on structures such as increase in vertical load, horizontal windy load needs to evaluate the current state of the constructions before renovating, especially the current state of the main structural system whether necessary to carry out repair and reinforcement or not. In addition, the inspection of the current status constructions before renovating is also the legal basis for the granting of construction permits to renovate and repair degraded works. Reinforced concrete buildings in the coastal areas in Vietnam, in particular, are working in the marine environment leading to damage the reinforced concrete construction. It should be significantly noted. Although there have been legal documents related to the inspection of constructions issued in Vietnam, the detailed contents and procedures of institution for each type of construction have not been mentioned yet. Therefore, the topic research paper of “research on technical solutions to renovate constructions with reinforced concrete structures in Vietnam” is to improve the quality and efficiency of construction. This investigation in Vietnam is very essential. This study uses the method of surveying the current state of the construction works in use, using the experimental sampling method to analyze and evaluate the damage of the work, then propose typical solutions to repair construction. The purpose of this study is to provide a process to check the damage of the works, and to propose solutions to repair them. This work is very important and has practical significance, helping managers to maintain works better.展开更多
Rice husk ask (RHA) is not a ultra-fine material as silica fume (SF),but possesses a very high specific surface area because of its porous structure. With the similar chemical composition of RHA and SF,the activity of...Rice husk ask (RHA) is not a ultra-fine material as silica fume (SF),but possesses a very high specific surface area because of its porous structure. With the similar chemical composition of RHA and SF,the activity of RHA,therefore,is different from that of SF. The objective of this work is to study the hydration and the microstructure of Portland cement blended with RHA in comparison with SF. The test results show that SF refined the pore structure of cement paste better than RHA. However,the effect of RHA on cement hydration is more pronouned than that of SF for the mixture with low water to binder ratio.展开更多
In this paper,we propose an approach to determine seven parameters of the Helmert transfor-mation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984(WGS84)to the Internati...In this paper,we propose an approach to determine seven parameters of the Helmert transfor-mation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984(WGS84)to the International Terrestrial Reference Frame.This includes(1)convert-ing the coordinates of common points from the global coordinate system to the local coordinate system,(2)identifying and eliminating outliers by the Dikin estimator,and(3)estimating seven parameters of the Helmert transformation by least squares(LS)estimation with the“clean”data(i.e.outliers removed).Herein,the local coordinate system provides a platform to separate points’horizontal and vertical components.Then,the Dikin estimator identifies and eliminates outliers in the horizontal or vertical component separately.It is significant because common points in a continuous GNSS network may contain outliers.The proposed approach is tested with the Géoazur GNSS network with the results showing that the Dikin estimator detects outliers at 6 out of 18 common points,among which three points are found with outliers in the vertical compo-nent only.Thus,instead of eliminating all coordinate components of these six common points,we only eliminate all coordinate components of three common points and only the vertical component of another three common points.Finally,the classical LS estimation is applied to“clean”data to estimate seven parameters of the Helmert transformation with a significant accuracy improvement.The Dikin estimator’s results are compared to those of other robust estimators of Huber and Theil-Sen,which shows that the Dikin estimator performs better.Furthermore,the weighted total least-squares estimation is implemented to assess the accuracy of the LS estimation with the same data.The inter-comparison of the seven estimated parameters and their standard deviations shows a small difference at a few per million levels(E-6).展开更多
文摘The Ba Lang sand beaches, located north of the Nha Trang Bay in Central Vietnam, are famous tourist attractions. However, they are experiencing shoreline and coastal erosion retreat, which is attributed to natural causes (such as tropical depressions, storms, and monsoons) as well as human impacts (such as hydropower generation, sand dredging, and coastal works). According to the forecast of the Vietnam Ministry of Natural Resources and Environment, global climate change will cause the sea level to rise by 74 cm along the coast from the Dai Lanh Cape to the Ke Ga Cape (including the Ba Lang beaches) by the end of this century in the representative concentration pathway (RCP) 8.5 scenario. Sea level rise (SLR) due to global climate change is expected to aggravate the coastal erosion and shoreline retreat problems. In this study, coupled numerical models with the spectral wave module (MIKE 21 SW), hydrodynamic module (MIKE 21 HD), and sand transport module (MIKE 21 ST) in the MIKE 21 package were used to simulate waves, current fields, and sediment dynamics along the Ba Lang beaches considering the impact of SLR. These models were calibrated with the field data measured in December 2016. The results showed that SLR caused the wave height to increase and reduced the current speed and total sediment load in monsoon conditions. The increase in wave height was even intensified under the joint impact of SLR and extreme events.
文摘The offshore renewable energy industry has been developing farms of floating offshore wind turbines in water depths up to 100 m.In Vietnam,floating offshore wind turbines have been developed to increase the production of clean and sustainable energy.The mooring system,which is used to keep the turbine stable and ensure the safety and economic efficiency of wind power production,is an important part of a floating offshore wind turbine.Appropriate selection of the mooring type and mooring line material can reduce the risks arising from the motion of wind turbines.Different types of mooring line material have been simulated and compared in order to determine the optimal type with the minimum motion risk for a floating wind turbine.This study focused on numerical modeling of semi-taut mooring systems using nonlinear materials for a semi-submersible wind turbine.Several modeling approaches common to current practice were applied.Hydrodynamic analysis was performed to investigate the motion of the response amplitude operators of the floating wind turbine.Dynamic analysis of mooring systems was performed using a time domain to obtain the tension responses of mooring lines under the ultimate limit states and fatigue limit states in Vietnamese sea conditions.The results showed that the use of nonlinear materials(polyester and/or nylon)for mooring systems can minimize the movement of the turbine and save costs.The use of synthetic fibers can reduce the maximum tension in mooring lines and the length of mooring lines.However,synthetic fiber ropes showed highly nonlinear load elongation properties,which were difficult to simulate using numerical software.The comparison of the characteristics of polyester and nylon mooring lines showed that the maximum and mean tensions of the nylon line were less than those of the polyester line.In addition,the un-stretched length of the polyester line was greater than that of the nylon line under the same mean tension load.Therefore,nylon material is recommended for the mooring lines of a floating offshore wind turbine.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’safety without interrupting their normal operations.This paper develops a novel framework using prompt engineering for seamlessly integrating users’domain knowledge about vibration signals with the advanced inference ability of well-trained large language models(LLMs)to accurately identify the actual states of structures.The proposed framework involves formulating collected data into a standardized form,utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals,and implementing an in-house program with the help of LLMs to perform damage detection.The advantages,as well as limitations,of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature,demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.
基金funded by Hanoi University of Civil Engineering(HUCE)in Project Code 35-2021/KHXD-TD.
文摘Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.
基金This work was financially supported by the Hanoi University of Civil Engineering(Vietnam),ID 28{2023/KHXD-TD}.
文摘The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation.The key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series data.The proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future values.The viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel frame.Furthermore,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.
文摘During the use of constructions, they will be degraded. Due to the negative impact on structures such as increase in vertical load, horizontal windy load needs to evaluate the current state of the constructions before renovating, especially the current state of the main structural system whether necessary to carry out repair and reinforcement or not. In addition, the inspection of the current status constructions before renovating is also the legal basis for the granting of construction permits to renovate and repair degraded works. Reinforced concrete buildings in the coastal areas in Vietnam, in particular, are working in the marine environment leading to damage the reinforced concrete construction. It should be significantly noted. Although there have been legal documents related to the inspection of constructions issued in Vietnam, the detailed contents and procedures of institution for each type of construction have not been mentioned yet. Therefore, the topic research paper of “research on technical solutions to renovate constructions with reinforced concrete structures in Vietnam” is to improve the quality and efficiency of construction. This investigation in Vietnam is very essential. This study uses the method of surveying the current state of the construction works in use, using the experimental sampling method to analyze and evaluate the damage of the work, then propose typical solutions to repair construction. The purpose of this study is to provide a process to check the damage of the works, and to propose solutions to repair them. This work is very important and has practical significance, helping managers to maintain works better.
文摘Rice husk ask (RHA) is not a ultra-fine material as silica fume (SF),but possesses a very high specific surface area because of its porous structure. With the similar chemical composition of RHA and SF,the activity of RHA,therefore,is different from that of SF. The objective of this work is to study the hydration and the microstructure of Portland cement blended with RHA in comparison with SF. The test results show that SF refined the pore structure of cement paste better than RHA. However,the effect of RHA on cement hydration is more pronouned than that of SF for the mixture with low water to binder ratio.
基金supported by the Ministry of Education and Training of Vietnam[B2020-XDA-05].
文摘In this paper,we propose an approach to determine seven parameters of the Helmert transfor-mation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984(WGS84)to the International Terrestrial Reference Frame.This includes(1)convert-ing the coordinates of common points from the global coordinate system to the local coordinate system,(2)identifying and eliminating outliers by the Dikin estimator,and(3)estimating seven parameters of the Helmert transformation by least squares(LS)estimation with the“clean”data(i.e.outliers removed).Herein,the local coordinate system provides a platform to separate points’horizontal and vertical components.Then,the Dikin estimator identifies and eliminates outliers in the horizontal or vertical component separately.It is significant because common points in a continuous GNSS network may contain outliers.The proposed approach is tested with the Géoazur GNSS network with the results showing that the Dikin estimator detects outliers at 6 out of 18 common points,among which three points are found with outliers in the vertical compo-nent only.Thus,instead of eliminating all coordinate components of these six common points,we only eliminate all coordinate components of three common points and only the vertical component of another three common points.Finally,the classical LS estimation is applied to“clean”data to estimate seven parameters of the Helmert transformation with a significant accuracy improvement.The Dikin estimator’s results are compared to those of other robust estimators of Huber and Theil-Sen,which shows that the Dikin estimator performs better.Furthermore,the weighted total least-squares estimation is implemented to assess the accuracy of the LS estimation with the same data.The inter-comparison of the seven estimated parameters and their standard deviations shows a small difference at a few per million levels(E-6).
