Artificial Neural Network Architecture Research Articles

Predicting asphaltene precipitation in CO2 flooding processes using artificial intelligence techniques

The removal of asphaltene depositions from crude oil wellbores and tubing is a very difficult and expensive process. Therefore, a very accurate predictor for the Asphaltene Precipitation (AP) is a very important tool for decision making. In this paper, the AP during the injection of carbon dioxide into crude oil reservoirs is forecasted using an artificial neural network (ANN). For this purpose, experimental data is collected from six different oil fields. 70% of the collected data is utilized for training and to develop the proposed ANN. Trainlm training algorithm was found to be the best ANN architecture for AP forecasting. In order to validate the capability of the designed ANN 30% of the unseen data is utilized for validation. The forecasting results show the Mean Square Error (MSE) of the forecasting is 0.0018 that confirms the high accuracy of the model. In order to evaluate the capability of the proposed methodology, the results of proposed ANN are compared with modified Hirschberg model. Comparison confirmed the proposed ANN has better capability and higher accuracy. In the final stage, the effects of various operating parameters on the AP during gas injection are examined. The results show that the most sensitive parameter is the reservoir temperature. In addition, there is a high correlation between an increase in the AP with an increase in carbon dioxide concentration in liquid phase.

DEEP LEARNING FOR SPACE DEBRIS REMOVAL

The advances in deep learning have revolutionized the field of artificial intelligence. These advances, as well as new tasks and requirements in space exploration, have led to an increased interest in these deep learning methods among space scientists and practitioners. The problems of controlling the attitude and relative motion of spacecraft are considered for both traditional and new missions, such as contactless space debris removal. Both supervised and reinforcement learning is used to solve such problems based on various architectures of artificial neural networks, including convolutional ones. The possibility of using deep learning together with methods of control theory is analyzed to solve the considered problems more efficiently. The difficulties that limit the application of these methods for space applications are highlighted. The necessary research directions for solving these problems are indicated.

Green synthesis of carbon nanotubes functionalized with iron nanoparticles and coffee husk biomass for efficient removal of losartan and diclofenac: Adsorption kinetics and ANN modeling studies

The presence of emerging contaminants in wastewater poses a global environmental challenge, requiring the development of innovative materials or methods for their treatment. This study focused on the production of green functionalized carbon nanotubes (CNTs) and using them in the adsorption of the pharmaceuticals Losartan (LOS) and Diclofenac (DIC). The efficiency of the methodology was verified by characterization techniques. Elemental composition analysis indicated a significant increase in the iron content after the green functionalization, proving the effectiveness of the method. Thermogravimetric analysis showed similar thermal degradation profiles for pristine CNTs and functionalized CNTs, indicating better post-functionalization thermal stability. BET analysis revealed mesoporous characteristics of CNTs, with increased surface area and pore volumes after functionalization. X-Ray diffraction confirmed the preservation of the lattice structure of the CNTs post-functionalization and post-adsorption, with changes in peak broadening suggesting surface modifications. LOS and DIC adsorption were evaluated via kinetic studies at four different concentrations (0.1–0.4 mmol/L) that were best represented by the pseudo-second order model, suggesting chemisorption mechanisms, with faster and higher uptakes for DIC (0.084–0.261 mmol/g; teq = 5 min) when compared to LOS (0.058–0.235 mmol/g; teq = 20 min). The curves were also studied via artificial neural networks (ANN) and revealed that the best ANN architecture for representing the experimental data is a network with [3 5 5 2] neurons trained using the Bayesian-Regularization algorithm and the Log-sigmoid (hidden layers) and Linear (output layer) transfer functions. The desorption study showed that CaCl2 had better performance in CNT regeneration, reaching its removal capacity above 50% up to 3 cycles, for both pharmaceuticals. These findings reveal the potential of the developed material as a promising adsorbent for targeted removal of pollutants, contributing to advances in the remediation of emerging contaminants and the application of artificial intelligence in adsorption research.

Artificial Neural Network Prediction Model for Maternal Health Services Quality in Nigeria

This study presents an innovative approach in predicting the quality of maternal health services in Nigeria using artificial neural networks (ANNs). It focuses on a binary classification task in categorizing maternal health services facilities into "High_Quality" and "Low_Quality" classes. Dataset was collected from diverse healthcare facilities in Nigeria through a WHO Health Resources and Services Availability Monitoring System (HeRAMS) cross sectional-survey report. The collected data was preprocessed; the ANN architecture was developed and trained using back-propagation method. The network was modeled using feed-forward modeling approach with 16 variables at the input layer, 2 hidden layers of 65 and 55 neurons each, and an output of 2 neurons. Hence, 87.17% accuracy with only 2.48% deviation at the 50 epoch was achieved. The model's performance was evaluated using standard metrics of accuracy (87%), precision (88%), recall (89%), and F1-score (88%) to assess its ability to accurately classify maternal health services facilities. The findings of the study can have significant implications to policymakers, healthcare providers, and maternal health advocates to optimize healthcare resources and achieve better maternal health outcomes in Nigeria. Data availability, potential biases in the data, and the subjective nature of some quality-related indicators were acknowledged as limitations. However, despite these challenges, the ANN classification model remains a valuable tool for supporting decision-making in maternal healthcare facility delivery service in Nigeria. Artificial neural network, Binary classification, Health services, Maternal health, Predictive modeling

A novel CFD-ANN approach for plunger valve optimization: Cost-effective performance enhancement

This paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD).

High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network

The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

Artificial neural networks ensemble methodology to predict significant wave height

The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the computational power increase, machine learning algorithms have been applied as surrogate to traditional numerical models, yielding comparative or better results. In this work, we present a methodology to create an ensemble of different artificial neural networks architectures, namely, MLP, RNN, LSTM, CNN and a hybrid CNN–LSTM, which aims to predict significant wave height on six different locations in the Brazilian coast. The networks are trained using NOAA’s numerical reforecast data and target the residual between observational data and the numerical model output. A new strategy to create the training and target datasets is demonstrated. Results show that our framework is capable of producing high efficient forecast, with an average accuracy of 80%, that can achieve up to 88% in the best case scenario, which means 5% reduction in error metrics if compared to NOAA’s numerical model, and a increasingly reduction of computational cost.

EPHA: An Energy-efficient Parallel Hybrid Architecture for ANNs and SNNs

Artificial neural networks (ANNs) and spiking neural networks (SNNs) are two general approaches to achieve artificial intelligence (AI). The former have been widely used in academia and industry fields; the latter, SNNs, are more similar to biological neural networks and can realize ultra-low power consumption, thus have received widespread research attention. However, due to their fundamental differences in computation formula and information coding, the two methods often require different and incompatible platforms. Alongside the development of AI, a general platform that can support both ANNs and SNNs is necessary. Moreover, there are some similarities between ANNs and SNNs, which leaves room to deploy different networks on the same architecture. However, there is little related research on this topic. Accordingly, this article presents an energy-efficient, scalable, and non-Von Neumann architecture (EPHA) for ANNs and SNNs. Our study combines device-, circuit-, architecture-, and algorithm-level innovations to achieve a parallel architecture with ultra-low power consumption. We use the compensated ferrimagnet to act as both synapses and neurons to store weights and perform dot-product operations, respectively. Moreover, we propose a novel computing flow to reduce the operations across multiple crossbar arrays, which enables our design to conduct large and complex tasks. On a suite of ANN and SNN workloads, the EPHA is 1.6× more power-efficient than a state-of-the-art design, NEBULA, in the ANN mode. In the SNN mode, our design is 4 orders of magnitude more than the Loihi in power efficiency.

Predictive Modeling of Fracture Behavior in Ti6Al4V Alloys Manufactured by SLM Process

This study focuses on ductile fracture behavior prediction for Ti6Al4V alloys fabricated via Selective Laser Melting (SLM). A modified Gurson-Tvergaard-Needleman (GTN) model characterizes void growth and shear mechanisms under uniaxial stress. The research explores the impact of Artificial Neural Network (ANN) architecture, specifically hidden layers and neurons, on predicting fracture parameters. Results reveal that increasing hidden layers substantially enhances accuracy, particularly for fracture displacement. Notably, predicting maximum force requires fewer layers than fracture displacement. Using selected layers and neurons, the system consistently achieved R2-values exceeding 0.99 for both maximum force and fracture displacement. The study identifies the initial void volume fraction (f0) parameter as having the most significant influence on both properties.

Using a sequence deep learning model to increase the acoustic context of a killer whale detector

Although Killer whales (Orcinus orca) produce many stereotypical vocalizations, their sounds can be difficult to identify in isolation. Experts often rely on acoustic context to accurately identify these animals acoustically. Automated detectors and classifiers, on the other hand, frequently rely on short clips that capture individual vocalizations, not leveraging information regarding previous sounds. We developed deep learning models that used 1-minute inputs containing from 0 to 50 calls, with the average clip having 18. We tested three artificial neural network architectures that used recurrent layers to take the sequence of acoustic events into account. As a baseline, we used a convolutional neural network that only took 3-s clips at a time, without considering sequences of events. Here, we present preliminary evaluations on a dataset containing 360 min with Southern Resident killer whale activity in the Salish Sea, and an equal amount of data without killer whale sounds. The best model used a combination of temporal convolutional layers and gated recurrent units to achieve a recall of 0.95 at the maximum precision of 0.98. The models will be applied to near real-time monitoring efforts and will be open-sourced in the future.

Understanding the effects of machinability properties of Incoloy 800 superalloy under different machining conditions using artificial intelligence methods

Incoloy 800 is frequently used in high-temperature applications as it has the ability to retain good metallurgical stability at elevated temperatures. Due to the nature of the applications used for, parts made from Incoloy 800 usually require different machining processes such as milling and turning. Therefore, the current study aims to investigate the milling performance of Incoloy 800 under different cutting parameters (75–150 m/min and 0.075–0.15 mm/rev) and cooling conditions namely dry, flood, Minimum Quantity Lubrication (MQL) and Cryogenic (Cryo)+MQL. It was observed that all machinability metrics improved in the MQL+Cryo C/L environment. It is noticeable that the surface roughness value improved by 30% in this environment. In addition, a model based on artificial neural networks (ANN) and particle swarm optimization (PSO) was proposed to analyze the results and predict optimum cutting parameters. It appears that Cryo+MQL strategies are the best option for all cutting parameters. It was found that the estimations for surface roughness, flank wear, and cutting temperature with the proposed ANN architecture are achieved with overall relative error of 6.08%, 12.38%, and 8.32%, respectively. The proposed model resulted in good performance between the experimental test data and the predicted values. The developed model made the most efficient predictions for the MQL+Cryo cutting environment. It was observed that the estimations of the different input parameters in the MQL+Cryo cutting environment present a relative error of 8.36%, 1.46%, and 2.38% for surface roughness, flank wear, and cutting temperature, respectively. By utilizing the predictive capability of the trained ANN model, the optimization of the input parameters was carried out with the PSO technique. Thus, with the developed PSO-ANN model, promising findings were obtained in overcoming important handicaps such as time and cost in experimental studies.

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data. Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead. Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases. The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.

PERFORMANCE COMPARISON OF U-NET AND LINKNET WITH DIFFERENT ENCODERS FOR REFORESTATION DETECTION

The subject of study is analysis of performance of artificial intelligence systems with different architectures for reforestation detection. The goal is to implement, train and evaluate system with different models for deforestation and reforestation detection. The tasks are to study problems and potential solutions in forestry for reforestation detection and present own solution. As part of model comparison, results are presented for different artificial neural network architectures with different encoders. For training and testing purpose custom dataset was created, which includes different areas of territory of Ukraine within different timestamps. Main research methods are literature analysis, experiment and case study. As a result of analysis of modern artificial intelligence methods, machine learning, deep learning and convolutional neural networks, high-precision algorithms U-Net and LinkNet were chosen for system implementation. Conclusions. The studied problem was stated formally and broken down in smaller steps; possible solutions were studied and proposed solution was described in details. Necessary mathematical background for analysis of the performance was provided. As part of the development, accurate deforestation/reforestation module was created. All analysis results were listed and a comparison of the studied algorithms was presented.

Artificial Neural Network and Multiple Regression Analysis Applied to 2D-QSAR Studies: the Case of Imidazolidine-2,4-dione as PTP1B Inhibitors

In this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine- 2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics.

Modeling the anaerobic digestion of palm oil mill effluent via physics-informed deep learning

The modeling of anaerobic digestion (AD) plays a significant role in process monitoring and prediction. Typically, mechanistic models or machine learning are used to model the AD process, where the former relies on the first-principle knowledge, while the latter learns the trend in data. In this study, models of AD were developed using data obtained from two palm oil mill effluent (POME) AD industrial plants, here they are denoted as plant A and plant B. The mechanistic model ADM1-R4 was first used to simulate POME AD to predict 3 AD outputs – methane, carbon dioxide, and effluent concentration. However, moderate prediction accuracy was observed, this could be due to highly dynamic environments in industrial plants and the incomplete knowledge of the mechanistic model where only 3 input features were considered. Then, artificial neural networks (ANNs) were trained using simulated data from the variational autoencoder for training and making the same predictions on the test data. ANNs were more inclusive as they included 6 or 7 input features and were found to make predictions with higher accuracy. Moreover, different ANN architectures were also investigated. Nevertheless, ANNs were time-consuming to train. Hence, physics-informed neural networks, namely ADM1-R4-NN, were further developed by embedding the information of mechanistic equations into the loss function of ANN. Overall, ADM1-R4-NN outperformed ANN and ADM1-R4, showing higher training efficiency (vs ANN) and testing accuracy (vs ANN and ADM1-R4). For plant A, the best prediction of ADM1-R4-NN resulted in R2 values of 0.87–0.95, while for plant B, the R2 was 0.81–0.88. Finally, feature importance analysis was conducted, where hydraulic retention time and mass loading were found to be the top influencing factors, while pretreatment temperature was a significant factor as well. The developed ADM1-R4-NN is a promising model for POME AD, and this method has the potential to be applied to other wastewater AD processes.

Recent Developments in Machine Learning for Mass Spectrometry.

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks.

The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-of-concept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23mmHg) outperforms the 1D BRNN (median RMSE of 4.25mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03mmHg) compared to the 3D CNN (±8.91mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model's training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model's accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic versus non-gothic) does not improve ANN performance for test cases sharing the same shape.

Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks.

Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm−3 MAE, and 0.03–52.30 mg Nm−3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10−6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Abstract Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and R 2 of 0.95, demonstrating adequate performance.