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Search results for: FIBER-REINFORCED CONCRETE BEAM, CHAINED MACHINE LEARNING MODEL, DUCTILITY INDEX, BENDING LOAD CAPACITY, ARTIFICIAL NEURAL NETWORKS
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublicationOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Prediction of metal deformation due to line heating; an alternative method of mechanical bending, based on artificial neural network approach
PublicationLine heating is one of the alternative methods of forming metals and this kind of forming uses the heating torch as a source of heat input. During the process, many parameters are considered like the size of the substrate, thickness, cooling method, source power intensity, the travel speed of the power source, the sequence of heating, and so on. It is important to analyze the factors affecting the...
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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
PublicationSince high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes...
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Dynamically positioned ship steering making use of backstepping method and artificial neural networks
PublicationThe article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type arti cial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. e arti cial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties....
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Modeling of Surface Roughness in Honing Processes by UsingFuzzy Artificial Neural Networks
PublicationHoning processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As...
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Artificial Neural Networks for Comparative Navigation
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TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
PublicationTensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing...
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Mathematical modeling and prediction of pit to crack transition under cyclic thermal load using artificial neural network
PublicationThe formation of pitting is a major problem in most metals, which is caused by extremely localized corrosion that creates small holes in metal and subsequently, it changes into cracks under mechanical load, thermo-mechanical stress, and corrosion process factors. This research aims to study pit to crack transition phenomenon of steel boiler heat tubes under cyclic thermal load, and mathematical modeling...
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A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublicationTraffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced...
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Analytical approach for membrane action in laterally-restrained reinforced concrete square slabs under uniformly distributed loads
PublicationLaterally-restrained reinforced concrete slabs can mobilise compressive membrane action and subsequent tensile membrane action under extreme loading conditions, thereby enhancing the load resistance under uniformly distributed loads. Previous analytical study focuses primarily on tensile membrane action in simply-supported slabs. This paper describes an analytical approach for membrane action in laterally-restrained square slabs....
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The method of analysis of damage reinforced concrete beams using terrestial laser scanning
PublicationThe authors present an analysis of the possibility to assess deformations and mechanisms of destructing bent reinforced concrete beams using the terrestrial laser scanning. As part of the experiments carried out at the Regional Laboratory of Construction of the Concrete Structures Division of the Civil and Environmental Engineering Faculty at Gdansk University of Technology, the reinforced concrete beams were subjected to destruction...
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Numerical Determination of the Load-Bearing Capacity of a Perforated Thin-Walled Beam in a Structural System with a Steel Grating
PublicationThis article presents the results of numerical simulations of a structural system consisting of steel perforated thin-walled beams and a steel grating. The simulations were conducted using the finite element method. The analysis took into account physical and geometric nonlinearity as well as the contact between the steel grating and the beams. The main goal of the research was to develop load-bearing curves for the main beam in...
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Load-carrying capacity and state of effort of tubes made of glass and basalt fibre reinforced polymer filled with concrete
PublicationOpisano badania eksperymentalne słupów wykonanych z rur polimerowych zbrojonych włóknami szklanymi i bazaltowymi. Część rur wypełniano wewnątrz betonem. Słupy z wypełnieniem niszczyły się w sposób powolny, wykazując wysoką nośność pokrytyczną, podczas gdy słupy z rur pustych niszczyły się w sposób gwałtowny, niesygnalizowany. Nośności uzyskane eksperymentalnie porównano do nośności obliczonych teoretycznie na podstawie procedur...
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Experimental investigations on concrete beams reinforced with CFRP lamellas
PublicationPaper presents experimental investigation made on two concrete beams reinforced with internal Carbon Fibre Reinforced Polymer (CFRP) lamellas (i.e. strips, bands). The reinforcement geometrical arrangement was similar as in normal concrete beams reinforced with steel bars and stirrups. The beams were destroyed by the shear forces, as intended. Obtained load-carrying capacities were lower as expected: below 40% of a calculated value....
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Mesoscopic simulations of a fracture process in reinforced concrete beam in bending using a 2D coupled DEM/micro-CT approach
PublicationW tej pracy zbadano numerycznie w warunkach 2D złożony proces pękania w krótkiej prostokątnej belce betonowej wzmocnionej jednym prętem podłużnym (bez zbrojenia pionowego) i poddanej quasi-statycznemu zginaniu w trzech punktach. Krytyczne pęknięcie poprzeczne w belce spowodowało jej uszkodzenie podczas doświadczenia. Symulacje numeryczne przeprowadzono klasyczną metodą elementów dyskretnych (DEM). Przyjęto trójfazowy opis betonu:...
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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
PublicationHigh-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram...
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Experimental investigations of damage evolution in concrete during bending by continuous micro-CT scanning
PublicationThe paper describes experimental investigation results of fracture in notched concrete beams under quasi-static three-point bending. To visualize 3D fracture in concrete under bending, an extended X-ray micro-computed tomography system was used, i.e. the tomography system SkyScan 1173 was connected to the loading machine ISTRON 5569. This combined system enabled to shot images of deforming concrete beams during a continuous deformation...
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THE METHOD OF ANALYSIS OF DAMAGE REINFORCED CONCRETE BEAMS USING TERRESTRIAL LASER SCANNING
PublicationThe authors present an analysis of the possibility to assess deformations and mode of failure of R-C beams using terrestrial laser scanning. As part of experiments carried out at the Regional Laboratory of Construction (at Gdansk University of Technology), reinforced concrete beams were subjected to destruction by bending and by shear. The process of press impact on the reinforced concrete beam was recorded using terrestrial laser...
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Evaluation of pounding effects between reinforced concrete frames subjected to far-field earthquakes in terms of damage index
PublicationIn this paper, three different damage indexes were used to detect nonlinear damages in two adjacent Reinforced Concrete (RC) structures considering pounding effects. 2-, 4- and 8-story benchmark RC Moment Resisting Frames (MRFs) were selected for this purpose with 60%, 75%, and 100% of minimum separation distance and also without any in-between separation gap. These structures were analyzed using the incremental dynamic analysis...
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Performance Analysis of Convolutional Neural Networks on Embedded Systems
PublicationMachine learning is no longer confined to cloud and high-end server systems and has been successfully deployed on devices that are part of Internet of Things. This paper presents the analysis of performance of convolutional neural networks deployed on an ARM microcontroller. Inference time is measured for different core frequencies, with and without DSP instructions and disabled access to cache. Networks use both real-valued and...
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COMPUTER-AIDED CONSTRUCTION AT DESIGNING REINFORCED CONCRETE COLUMNS AS PER EC
PublicationThe article presents the author’s computer program for designing and dimensioning columns in reinforced concrete structures taking into account phenomena affecting their behaviour and information referring to design as per EC. The computer program was developed with the use of C++ programming language. The program guides the user through particular dimensioning stages: from introducing basic data such as dimensions, concrete...
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Response of a fiber-optic Fabry-Pérot interferometer to refractive index and absorption changes – modelling and experiments
PublicationThis paper describes how the refractive index and the absorption of investigated substances change the spectrum of the optical radiation at the output of the fiber-optic Fabry-Pérot interferometer. The modeling of the operation of the interferometer takes into account not only the spectra of the refractive index and the absorption of the medium that is inside the cavity, but also spectra of the refractive indices of the core and...
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Modelling reinforced concrete beams under mixed shear-tension failure with different continuous FE approaches
PublicationThe paper presents quasi-static numerical simulations of the behaviour of short reinforced concrete beams without shear reinforcement under mixed shear-tension failure using the FEM and four various constitutive continuum models for concrete. First, an isotropic elasto-plastic model with a Drucker-Prager criterion defined in compression and with a Rankine criterion defined in tension was used. Next, an anisotropic smeared crack...
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Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
PublicationA problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included...
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Analysis of sloping brace stiffness influence on stability and load bearing capacity of a truss
PublicationThe paper is focused on the numerical study of stability and load bearing capacity of a truss with side elastic braces. The structure is made in reality. The rotational and sliding brace stiffnesses were taken into account. Linear buckling analysis and non-linear static analysis with geometric and material nonlinearity were performed for the beam and shell model of the truss with respect to the angle of sloping braces. As a result...
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REINFORCED CONCRETE SUPPORTING CONSTRUCTION OF THE STADIUM COVER FOR EURO 2012 IN GDAŃSK
PublicationIn the article structural issues that connected with the reinforced concrete supporting construction of the stadium roofing for EURO 2012 in Gdańsk were described. In the first part of the article the concept of stadium foundation were described. In the second the static - strength analysis for two variants fastening together individual foundation elements were made. The two assumed geometrically different concept of foundation...
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The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification
PublicationDeveloping of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and...
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The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process
PublicationThis paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in...
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Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks
PublicationDeep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturba-tions are unnoticeable for humans. Such attacks...
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Evaluation of Asphalt Mixture Low-Temperature Performance in Bending Beam Creep Test
PublicationLow-temperature cracking is one of the most common road pavement distress types in Poland. While bitumen performance can be evaluated in detail using bending beam rheometer (BBR) or dynamic shear rheometer (DSR) tests, none of the normalized test methods gives a comprehensive representation of low-temperature performance of the asphalt mixtures. This article presents the Bending Beam Creep test performed at temperatures from −20...
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublicationCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
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Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model
PublicationThis work attempts to meet the challenges associated with the classification of LIDAR point clouds by means of deep learning. In addition to achieving high accuracy, the designed system should allow the classification of point clouds covering an area of several dozen square kilometers within a reasonable time interval. Therefore, it must be characterized by fast processing and efficient use of memory. Thus, the most popular approaches...
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Artificial Neural Networks in Microwave Components and Circuits Modeling
PublicationArtykuł dotyczy wykorzystania sztucznych sieci neuronowych (SNN) w projektowaniu i optymalizacji układów mikrofalowych.Zaprezentowano podstawowe zasady i założenia modelowania z użyciem SNN. Możliwości opisywanej metody opisano wykorzystując przykładowyprojekt anteny łatowej. Przedstawiono różne strategie modelowania układów, które wykorzystują możliwości opisywanej metody w połączeniu zwiedzą mikrofalową. Porównano również dokładność...
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SIMPLIFIED DYNAMIC MODEL OF ROTATING BEAM
PublicationIn the paper a hybrid model of rotating beam is presented. It was obtained by using two methods: modal decomposition and spatial discretization. Reduced modal model was built for the system without the load related to inertia forces that occur during beam rotation. This inertia load was next modeled by using the method of simply spatial discretization and combined with reduced modal model. This approach allows to obtain accurate...
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Explainable machine learning for diffraction patterns
PublicationSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
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A survey of neural networks usage for intrusion detection systems
PublicationIn recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commer‐ cial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublicationIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublicationThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublicationA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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INFLUENCE OF DATA NORMALIZATION ON THE EFFECTIVENESS OF NEURAL NETWORKS APPLIED TO CLASSIFICATION OF PAVEMENT CONDITIONS – CASE STUDY
PublicationIn recent years automatic classification employing machine learning seems to be in high demand for tele-informatic-based solutions. An example of such solutions are intelligent transportation systems (ITS), in which various factors are taken into account. The subject of the study presented is the impact of data pre-processing and normalization on the accuracy and training effectiveness of artificial neural networks in the case...
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Model of distributed learning objects repository for a heterogenic internet environment
PublicationW artykule wprowadzono pojęcie komponentu edukacyjnego jako rozszerzenie obiektu edukacyjnego o elementy zachowania (metody). Zaproponowane podejście jest zgodne z paradygmatem obiektowym. W oparciu o komponent edukacyjny zaprojektowano model budowy repozytorium materiałów edukacyjnych. Model ten jest oparty o usługi sieciowe i rejestry UDDI. Komponent edukacyjny oraz model repozytorium mogą znaleźć zastosowanie w konstrukcji zbiorów...
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A concept of heterogeneous numerical model of concrete for GPR simulations
PublicationThe Ground Penetrating Radar (GPR) method, which is increasingly being used in the non-destructive diagnostics of reinforced concrete structures, often needs more accurate interpretation tools for analysis of experimental data. Recently, there has been growing interest in developing of various numerical models for exhaustive understanding of GPR data. This paper presents the concept of a heterogeneous numerical model of concrete,...
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OPTICAL STRAIN MEASUREMENT OF CONCRETE VERSUS MANUAL EXTENSOMETER MEASUREMENT BASED ON THE TEST RC DEEP BEAM IN A COMPLEX STATE OF STRESS
PublicationThe purpose of this study is to compare the strain measurement techniques of concrete in R-C element subjected to the monotonic load up to the failure. In the analysis manual extensometer methods of measurements and the optical system ARAMIS for non-contact three-dimensional measurements of deformation was used. The test sample was a cantilever deep beam loaded throughout the depth which was a part of the reinforced concrete deep...
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Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks
PublicationObject detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction...
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Debonding Size Estimation in Reinforced Concrete Beams Using Guided Wave-Based Method
PublicationThe following paper presents the results of the theoretical and experimental analysis of the influence of debonding size on guided wave propagation in reinforced concrete beams. The main aim of the paper is a development of a novel, baseline-free method for determining the total area of debonding between steel rebar embedded in a concrete cover on the basis of the average wave velocity or the time of flight. The correctness of...
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Stability and load bearing capacity of a truss with elastic braces
PublicationThe present paper is devoted to the numerical and experimental investigations of stability of a truss stiffened by elastic braces. The model of a real roof truss scaled by factor ¼ was investigated. In the research the linear buckling and non-linear static analysis of the truss shell and beam model with geometric and material non-linearity is presented. The initial imperfections were assumed in the form of the first buckling mode....
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How Machine Learning Contributes to Solve Acoustical Problems
PublicationMachine learning is the process of learning functional relationships between measured signals (called percepts in the artificial intelligence literature) and some output of interest. In some cases, we wish to learn very specific relationships from signals such as identifying the language of a speaker (e.g. Zissman, 1996) which has direct applications such as in call center routing or performing a music information retrieval task...
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Examination of advanced isotropic constitutive laws under complex stress states in plain and reinforced concrete specimens
PublicationThe performance of advanced isotropic constitutive laws under complex stress states in plain and reinforced concrete specimens is investigated. Three different formulations are chosen: original Mazars model, Mazars mi model and model proposed by Pereira and coworkers. The degradation of the material in all formulations is described via a single variable, but a strain/stress state is taken into account via quite sophisticated relationships....