Improved LSTM deep learning network approach for enhanced creep prediction in concrete-filled steel tubes

The methodology for evaluating the fire resistance performance of concrete-filled steel tube columns by integrating conditional tabular generative adversarial networks and random oversampling

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

Combined compression-bending performance and design of CFST with localised pitting corrosion

The use of artifical intelligence (AI) and machine learning (ML) approaches in various scientific and engineering disciplines has grown exponentially over recent years. This upsurge also includes applications of physics-guided ML models and explainable AI. However, in addition to the dificulties involved in the identification of relevant model inputs, the advantages, contributions, and credibility of ML models are still open challenges, especially when these models are evaluated against the perceptual hydrologic understanding of the system in question. In this study, we aim to investigate some of these challenges using the case of seasonal streamflow forecasting with lead times up to three months in several hydrologically challenging river basins of prairie provinces of Canada (i.e., Alberta, Saskatchewan, and Manitoba).Multiple ML techniques, including Random Forest (RF) and Long Short-Term Memory (LSTM) models, are used to produce ensemble forecasts for 135 sub-basins of the Nelson-Churchill River Basin, comprising the vast area from the Rocky mountains up to the Hudson Bay, with the monthly temporal resolution and spatial scales of the order of 200 km2 to ~1.0 x106 km2, as reflected by drainage areas of all sub-basins. A large set of potential inputs (105 predictors) is used in this study. These potential inputs include hydrometeorological variables derived from the Daymet database, Environment and Climate Change Canada’s hydrometric network, and hydrometeorological forecasts from the European Centre for Medium-Range Weather Forecasts, and various static attributes of all sub-basins.The Pearson’s correlation coefficient (CC) and Partial Mutual Information (PMI) were used, as model agnostic methods, to analyze the set of potential predictors and identify the most appropriate inputs for seasonal flow forecasting, prior to ML model development. Subsequently, modeling experiments were designed to investigate the ML model performance and test the usefulness of CC and PMI based techniques on modeling results. The model-agnostic and model-dependent findings were compared and analyzed in light of the perceptual understanding of the hydrological system. Furthermore, the Convergent Cross-Mapping (CCM) method was used with selected variables to further explore the causal, rather than correlational, relationships and interpret the results with the aim of developing ethical and responsible ML (ERML) models. We define ERML models as data driven models that are transparent and hydrologically explainable.The preliminary results of this study indicate that PMI is quite effective in filtering some of the CC-based selections, which might form multiple equifinale sets of predictors. This step is critical for identifying the most relevant and necessary inputs. In spite of the coarse spatial and temporal resolutions, which complicate crisp hydrologic perceptions, the CCM method seems to support the selection of various input variables with hydrologic causality, strengthening the transparency and credibility of ML models.

Self-compacting concrete is quite commonly used in concrete-filled steel tube structures, but the compaction level of the self-compacting concrete, that is, the percentage of volume occupied by materials other than air void, within the steel tube is seldom investigated. The authors are of the view that the concrete mix proportions of the self-compacting concrete may have significant effects on the compaction level of the self-compacting concrete, which will be quantified by the ‘compaction index’ proposed in this study and thus the performance of the concrete-filled steel tube. Moreover, the mix proportions would also influence the performance of the concrete-filled steel tube by affecting the aggregate–aggregate and aggregate–paste interactions of the concrete, albeit this important issue is rarely addressed in previous studies either. Herein, a pilot study is conducted to investigate the influences of the self-compacting concrete mix proportions on the axial performance of concrete-filled steel tube. Four groups of concrete-filled steel tube specimens made with different self-compacting concrete were tested, and the investigated concrete mix parameters included the paste volume, fine to coarse aggregate ratio, and 9.5–19.0 mm aggregate ratio. It was found that the compaction index of the self-compacting concrete is a key factor enabling the successful use of self-compacting concrete in concrete-filled steel tube. Moreover, the paste volume and aggregate proportions of the concrete mix have certain effects on the post-peak behaviour and ductility of concrete-filled steel tube.

Three-level evaluation method of cumulative slope deformation hybrid machine learning models and interpretability analysis

Machine learning-based residual drift prediction of concrete-filled steel tube columns under earthquake loads

Advancements in interfacial bonding performance of concrete-filled steel tube members

The exploration and development of shale gas is becoming more important owing to the increasing of world energy demand. However, calculating the productivity of horizontal wells after shale gas volume fracturing is always difficult due to various complicated factors. In this study, the long short-term memory (LSTM) neural network was establised and demonstrated to be successful in China complex shale gas production time series prediction. Firstly, the geological characteristics of shale gas and fracturing technology was briefly introduced. Then, a shale gas horizontal well volume fracturing productivity prediction model was established based on a long short-term memory (LSTM) neural network and using actual production data for two shale gas models. The mean absolute percentage error between the predicted results and the actual production data is less than 5%, which indicates a good performance in terms of the prediction of values and trends. Based on this model, sensitivity analysis of the effect of the stimulated reservoir volume (SRV), fracture parameters, permeability, and other factors on the productivity of shale gas wells was carried out. The newly developed LSTM time series productivity prediction method and the insights it provides can be used by reservoir engineers to optimize shale gas field development plans. Highlights A new machine learning (LSTM) shale gas production prediction model is proposed. The new machine learning model is better than the traditional RTA or DCA methods. The example calculation results show that the LSTM can predict the future production capacity value with a certain accuracy. The new model is useful for optimization in shale gas field development.

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

With the increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, Gradient Boost Regressor (GBR) and Long Short-Term Memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements, and the others applying a two-step approach, first estimating lake nutrients that have limited observations, and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed by using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training/testing year combinations.

With the increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, Gradient Boost Regressor (GBR) and Long Short-Term Memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements, and the others applying a two-step approach, first estimating lake nutrients that have limited observations, and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed by using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training/testing year combinations.

<strong class="journal-contentHeaderColor">Abstract.</strong> With increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, the gradient boost regressor (GBR) and long short-term memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements and the others applying a two-step approach, first estimating lake nutrients that have limited observations and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training–testing year combinations.

With the increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, Gradient Boost Regressor (GBR) and Long Short-Term Memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements, and the others applying a two-step approach, first estimating lake nutrients that have limited observations, and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed by using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training/testing year combinations.

With the increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, Gradient Boost Regressor (GBR) and Long Short-Term Memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements, and the others applying a two-step approach, first estimating lake nutrients that have limited observations, and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed by using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training/testing year combinations.