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Gdańsk University of Technology

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Publications from the year 2023

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  • Optimal Design of Transmitarray Antennas via Low-Cost Surrogate Modelling
    • Mehmet Belen
    • Alper Caliskan
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Peyman Mahouti
    2023 Full text Scientific Reports

    Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the design process of transmitarrays is more intricate due to the necessity of manipulating both the transmission phase and magnitude of its unit elements. For reliability, the design process has to be conducted at the level of full-wave electromagnetic (EM) models, which makes direct optimization prohibitive. The most widely used workaround is to employ surrogate modeling techniques to construct fast representations of the unit elements, yet the initial model setup cost is typically high and includes acquisition of thousands of training data points. In this paper, we propose a novel approach to cost-efficient design of transmitarrays. It is based on artificial-intelligence (AI)-enabled data-driven surrogates, which can be constructed using only a few hundreds of training data samples, while exhibiting the predictive power sufficient for reliable design. Our methodology is demonstrated by re-using the presented surrogate for the design of high-performance transmitarrays operating at various frequency ranges of 8-14 GHz, 22-28 GHz, and 28-36 GHz.

  • Optimal retrofit strategy using viscous dampers between adjacent RC and SMRFs prone to earthquake‑induced pounding
    • Neda Asgarkhani
    • Farzin Kazemi
    • Robert Jankowski
    2023 Full text Archives of Civil and Mechanical Engineering

    Nowadays, retrofitting-damaged buildings is an important challenge for engineers. Finding the optimal placement of Viscous Dampers (VDs) between adjacent structures prone to earthquake-induced pounding can help designers to implement VDs with optimizing the cost of construction and achieving higher performance levels for both structures. In this research, the optimal placement of linear and nonlinear VDs between the 3-story, 5-story, and 9-story Steel and RC Moment-Resisting Frames (SMRFs and RC MRFs) is investigated. It is shown that the pounding phenomenon can significantly affect the seismic performance capacities of buildings during earthquakes, and using VDs can improve the seismic limit-state capacities of buildings for retrofitting purposes. For this goal, the seismic limit-state capacities of both colliding structures were assessed using Incremental Dynamic Analysis (IDA) assuming Near-fault Pulse-Like, Near-fault No-Pulse, and Far-Fault seismic records suggested by FEMA-P695. To perform IDAs, structures were modeled according to the seismic codes using a developed algorithm in Matlab and OpenSees software with the ability to remove a collapsed structure during the analysis. The results present an optimal placement for using VDs between structures and also compare the possible conditions to implement VDs. Using these results, engineers can approximately predict the seismic performance levels of both structures prone to earthquake-induced pounding and their final performance after retrofitting. Finally, retrofitting modification factors were proposed to help designers to predict the limit-state performance levels of retrofitted colliding structures without involving complicated and time-consuming analyses.

  • Optimisation of cooperation of hybrid renewable energy sources with hydrogen energy storage toward the lowest net present cost
    • Jakub Łukasik
    • Marcin Jewartowski
    • Jan Wajs
    2023 Full text Instal

    The paper presents the results of a technical and economic analysis of the power supply for a model industrial facility based on intermittent renewable energy sources in the form of wind turbines and photovoltaic modules, supplemented with hydrogen energy storage. The adopted power supply strategy assumed the maximisation of self-consumption of self-produced electricity. Six variants were considered, including two with an energy storage system, three using only RES, and a reference variant in which the model facility is powered by the power grid. The modelling and optimisation of the proposed variants was carried out in the HOMER software, in terms of the lowest net present cost. The results obtained indicate that the most advantageous configuration is a grid-connected hybrid renewable energy system consisting of wind turbines and a photovoltaic power plant. A system with hydrogen energy storage is much more profitable than powering the facility from the grid. The profitability of hydrogen energy storage increases even more with the projected increase in electricity prices and the falling prices of hydrogen system components.

  • Optimization and Modeling of Cr (VI) Removal from Tannery Wastewater onto Activated Carbon Prepared from Coffee Husk and Sulfuric Acid (H2SO4) as Activating Agent by Using Central Composite Design (CCD)
    • Worku Firomsa Kabeta
    • Temesgen Amibo
    • Surafel Mustafa Bayan
    • Abreham Bekele Bayu
    2023 Full text Journal of Environmental and Public Health

    The primary goal of this research is to lower the hexavalent chromium (Cr (VI)) concentration that has occurred from the growth of the tannery industry. As a result, the potential for heavy metal concentration is increasing day by day. Industrial effluent containing Cr (VI) contributes significantly to water pollution. Chromium hexavalent ion (Cr (VI)) in wastewater is extremely hazardous to the environment. It is critical to address such a condition using activated carbon derived from biomass. Adsorption is one of the most successful methods for removing hexavalent chromium from wastewater. Treated wastewater has no substantial environmental contamination consequences. The ash content, moisture content, volatile matter content, and fixed carbon content of wet coffee husk were 3.51, 10.85, 68.33, and 17.31, respectively. The physicochemical properties of coffee husk-based activated carbon (CHBAC) obtained during experimentation were pH, porosity, the yield of CHBAC, bulk density, point of zero charges, and specific surface area of 5.2, 58.4 percent, 60.1 percent, 0.71 g/mL, 4.19, and 1396 m2/g, respectively, indicating that CHBAC has a higher capacity as an adsorbent medium. For optimization purposes, the parameters ranged from pH (0.3–3.7), dose (2.3–5.7) , and contact time (0.3–3.7) hr. The quadratic models were chosen for optimization, and the value for the model was significant since it was less than 0.05, but the lack of fit model was inconsequential because it was more than 0.05. The optimum adsorption obtained with numerical optimization of Cr (VI) was 97.65 percent. This was obtained at a pH of 1.926, a dose of 4.209 g/L, and a contact time of 2.101 hours. This result was observed at a pH of 1.93, a dosage of 4.2 g/L, and a contact duration of 2.1 hours. The desirability obtained during numerical optimization was 1. Coffee husk-based activated carbon has a bigger surface area, and it has a stronger ability to absorb hexavalent chromium from tannery wastewater effluents.

  • Optimization of a Fabric Phase Sorptive Extraction protocol for the isolation of six bisphenols from juice pouches to be analysed by high performance liquid chromatography coupled with diode array detector
    • Paweł Kubica
    • Natasa Kalogiouri
    • Abuzar Kabir
    • Kenneth G. Furton
    • Victoria F. Samanidou
    2023 JOURNAL OF CHROMATOGRAPHY A

    Fabric Phase Sorptive Extraction (FPSE) combined with high pressure liquid chromatography using to diode array detection (HPLC-DAD) was applied for the simultaneous determination of bisphenols (BPA, BPB, BPC, BPE, BPF, BPS) in juice pouches. The FPSE procedure was optimized with regards to the critical parameters that affect the performance of the method including the selection of the FPSE membrane type and size, adsorption time, extraction time, solvent volume desorption, magnetic stirring ratio, and salt addition. The FPSE membrane could be reused up to 14 times. The developed FPSE-HPLC-DAD method was validated in terms of linearity, sensitivity, accuracy andprecision. The limits of detection (LODs) were lower than 6.9 ng/mL, while the limits of quantification (LOQs) were lower than 21 ng/mL. The results obtained are satisfactory in terms of precision, accuracy and repeatability, with recoveries above 86% and CV values below 9.5%. The FPSE-HPLC-DAD method was successfully applied in the determination of six bisphenols in juice samples stored in pouches.

  • Optimization of adsorption of methyl orange from aqueous solution by magnetic CoFe2O4/ZnAl-layered double hydroxide composite using response surface methodology
    • Yiene Molla Desalegn
    • Endrias Adane Bekele
    • Temesgen Amibo
    • Temesgen Debelo Desissa
    2023 Full text Materials Research Express

    The CoFe2O4/ZnAl-layered double hydroxide (LDH) composite was successfully developed through a facile co-precipitation method, characterized, and applied as an effective adsorbent for the removal of methyl orange (MO) dye from aqueous solutions. The central composite design (CCD) of the response surface methodology (RSM) was employed to estimate and optimize process variables such as initial MO concentrations, solution pH, adsorbent dosage, and contact time. 98.878% adsorption efficiency was obtained at an initial concentration of 18.747 mg l−1 of MO, with an adsorbent dosage of 0.048 g, a solution pH of 2.770, and a contact time of 85.890 min. Analysis of variance (ANOVA) confirmed the significance of the predicted model (R2 = 0.9844). Kinetic and equilibrium studies indicated that the experimental data for MO adsorption were best described by pseudo-second-order kinetic and Langmuir models. The maximum monolayer adsorption capacity of the CoFe2O4/ZnAl-LDH for MO was 42.3 mg g−1.

  • Optimization of Bread Production Using Neuro-Fuzzy Modelling
    • Tomasz Boiński
    • Julian Szymański
    2023

    Automation of food production is an actively researched domain. One of the areas, where automation is still not progressing significantly is bread making. The process still relies on expert knowledge regarding how to react to procedure changes depending on environmental conditions, quality of the ingredients, etc. In this paper, we propose an ANFIS-based model for changing the mixer speed during the kneading process. Although the recipes usually indicate the time for which the mixing should be done using slow and fast mixing speeds, however, it is the human, who makes the final decision as the mixers differ in terms of the mixing quality, speed, etc. Furthermore, unexpected differences in flour quality or room conditions can impact the time required to mix the ingredients. In the paper, different methods for fuzzy modeling are described and analyzed. The tested models are compared using both generated and real data and the best solution is presented.

  • Optimization of parallel implementation of UNRES package for coarse‐grained simulations to treat large proteins
    • Adam Sieradzan
    • Jordi Sans‐Duñó
    • Emilia Lubecka
    • Cezary Czaplewski
    • Agnieszka Lipska
    • Henryk Leszczyński
    • Krzysztof Ocetkiewicz
    • Jerzy Proficz
    • Paweł Czarnul
    • Henryk Krawczyk
    • Adam Liwo
    2023 Full text JOURNAL OF COMPUTATIONAL CHEMISTRY

    We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size. Parallel performance of the code and comparison of its performance with that of AMBER, GROMACS and MARTINI 3 is presented.

  • Optimization of the distance between the vertical plates in the convective air heat exchanger
    • Michał Ryms
    • Krzysztof Tesch
    • Witold Lewandowski
    2023 INTERNATIONAL JOURNAL OF THERMAL SCIENCES

    This paper examines the influence of the distance between vertical plates on the intensity of free convective heat transfer along with the optimization of this distance. Experimental tests were carried out for one model channel of such an heat exchanger with widths , 0.085 and 0.18 m. This channel, open at the top and sides, was formed by two isothermal symmetrically heated parallel vertical plates of dimensions m and m. The influence of the heating surface temperatures , 40, 50, 55, 60 and 70 °C on the convective temperature fields and velocity generated inside the channels was investigated. Directly measured temperature fields, as well as velocity fields measured indirectly using the NRP, enabled the thermodynamic parameters of the heat exchanger to be determined. Based on the temperature gradient distribution on the wall, its average value was determined for each of the plates and for the entire channel, after which the heat flux transferred from the plates was calculated. The heat flux transferred with the air and the efficiency of heat transfer in the channel were determined using the balance method based on the average temperatures and air velocities at the inlet and and at the outlet and of the channel obtained from the temperature and velocity fields. A grid placed vertically in the channel, halfway across the panel width and perpendicular to the heating surfaces was used to detect the temperature field in air. The image and matrix of these temperatures were determined using a thermal imaging camera. The numerical reconstruction procedure (NRP) was used to determine the velocity field.

  • Optimization of the femtosecond laser impulse for excitation and the Spin-Orbit mediated dissociation in the NaRb Dimer
    • Jan Kozicki
    • Patryk Jasik
    • Tymon Kilich
    • Józef Sienkiewicz
    2023 Full text JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER

    We study the dynamics of multiple coupled states under the influence of an arbitrary time-dependent external field to investigate the femtosecond laser-driven excitation and the spin-orbit mediated dissociation in the NaRb dimer. In this process, the dimer is excited from the ground triplet state 1^3Sigma+ to the 1^3Pi state using the femtosecond laser impulse and the spin-orbit coupling between the 1^3Pi and 2^1Sigma+ states results in the singlet-triplet transition. The laser impulse parameters are optimised to obtain maximum yield in electronic states correlating with the first excited atomic asymptote. We observe the detailed population statistics and power-law decay of these states. Finally, the analysis of the population oscillations allows us to determine the optimal time delay for dumping the molecule to its absolute ground state.

  • Optimization of the Hardware Layer for IoT Systems using a Trust Region Method with Adaptive Forward Finite Differences
    • Adrian Bekasiewicz
    2023 Full text IEEE Internet of Things Journal

    Trust-region (TR) algorithms represent a popular class of local optimization methods. Owing to straightforward setup and low computational cost, TR routines based on linear models determined using forward finite differences (FD) are often utilized for performance tuning of microwave and antenna components incorporated within the Internet of Things systems. Despite usefulness for design of complex structures, performance of TR methods vastly depends on the quality of FD-based local models. The latter are normally identified from perturbations determined a priori using either rules-of-thumb, or as a result of manual tuning. In this work, a framework for automatic determination of FD steps and their adjustment between the TR algorithm iterations is proposed. The method involves numerical optimization of perturbations so as to equalize the objective function changes w.r.t. the center design to the desirable precision. To maintain acceptable cost, the FD-tuning procedure is executed using the same approximation model as the one exploited in the course of the structure optimization. The proposed framework has been tested on a total of twelve design problems. Furthermore, the presented method has been thoroughly validated against TR-algorithms with static, a priori selected perturbations. Numerical results indicate that the proposed framework provides up to 50% performance improvement (in terms of the optimized designs quality) compared to the state-of-the-art TR-based approaches. Usefulness of the proposed method for the real-world Internet of Things systems has been implicitly demonstrated through utilization of one of the optimized structures in a hardware layer of a real-time localization system.

  • Optimization of the System for Determining the Volume of Tissue Needed for Breast Reconstruction
    • Julia Czałpińska
    • Andżelika Janicka
    • Jakub Rzepkowski
    • Mariusz Kaczmarek
    • Tomasz Kocejko
    • Jo Kang-Hyun
    2023

    This article presents techniques for reconstructing surfaces and volume calculations using a point cloud generated from 3D imaging. The main objective of this article was to optimize the voxel size for the most accurate representation of the surface of the female breast. We experimented with different methods for determining volume using images from the Intel D435i camera. In addition, we designed application and measurement station tailored specifically to the clinical requirements. Ultimately, our results show that 3D imaging systems can effectively determine breast volume for surgical procedures.

  • Optimization of vortex-assisted hydrophobic magnetic deep eutectic solvent-based dispersive liquid phase microextraction for quantification of niclosamide in real samples
    • Nail Altunay
    • Roberto Castro Munoz
    • Hameed Haq
    2023 FOOD CHEMISTRY

    In this manuscript, a green and fast vortex-assisted hydrophobic magnetic deep eutectic solvent-based dispersive liquid phase microextraction (VA-HMDES-DLPME) method was developed for the selective extraction and determination of niclosamide in read samples, including rice, medicine tablets, and water samples. Here, hydrophobic magnetic deep eutectic solvents were used as the extracting solvent without requiring any centrifugation step. In the light of preliminary experiments, important parameters, such as volume of extraction solvent, pH, acetonitrile volume and vortex time, affecting the extraction efficiency of niclosamide were optimized using a Box–Behnken design. The linear dynamic range (0.25–120 µg/L), the limit of detection (0.08 µg/L), the limit of quantitation (0.25 µg/L), preconcentration factor (1 8 0), and enrichment factor (1 3 0) of the method were determined using optimized data. In particular, the validation parameters of the optimized VA-HMDES-DLPME, including robustness, matrix effect accuracy, and precision, were investigated. In addition to this, intra- and inter-day precisions were determined as ≤3.5 % and ≤4.1%, respectively. Finally, the optimized method was successfully used for the extraction of niclosamide in the selected samples prior to spectrophotometric analysis.

  • Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls
    • Masoomeh Mirrashid
    • Hosein Naderpour
    • Denise-Penelope N. Kontoni
    • Anna Jakubczyk-Gałczyńska
    • Robert Jankowski
    • Tan N. Nguyen
    2023 Full text Buildings

    This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls.

  • Optimized Deep Learning Model for Flood Detection Using Satellite Images
    • Andrzej Stateczny
    • Hirald Dwaraka Praveena
    • Ravikiran Hassan Krishnappa
    • Kanegonda Ravi Chythanya
    • Beenarani Balakrishnan Babysarojam
    2023 Full text Remote Sensing

    The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

  • Optimized photodegradation of palm oil agroindustry waste effluent using multivalent manganese–modified black titanium dioxide
    • Rab Nawaz
    • Sajjad Haider
    • Muzammil Anjum
    • Vipin Kumar Oad
    • Adnan Haider
    • Rawaiz Khan
    • Muhammad Aqif
    • Tahir Hanif
    • Nasruulah Khan
    2023 Full text ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

    This article presents a methodological approach to use manganese (Mn3+Mn7+)-modified black titanium dioxide (Mn/BTiO2) as a photocatalyst to optimize and improve visible-light-driven photodegradation of treated agro-industrial effluent (TPOME). A modified wet chemical process was used to prepare BTiO2. The BTiO2 was then wet impregnated with Mn and calcined at 300 °C for 1 h to produce Mn/BTiO2. The activity of Mn/BTiO2 was investigated in terms of photo-assisted elimination of chemical oxygen demand (COD), phenolic compounds (PCs), color, and total organic carbon (TOC). Using the design of experiments (DOE), the conditions of the photocatalytic process, including photocatalyst loading, Mn concentration, hydrogen peroxide (H2O2) dose, and irradiation time, were optimized. Under the optimum conditions (0.85 g/L photocatalyst loading, 0.048 mol/L H2O2 dose, 0.301 wt.% Mn concentration, and 204 min irradiation time) COD, PCs, color, and TOC removal efficiencies of 88.87%, 86.04%, 62.8%, and 84.66%, respectively, were obtained. Statistical analysis showed that the response variable’s removal from TPOME estimation had high R2 and low RMSE, MSE, MAD, MAE, and MAPE values, indicating high reliability. This study demonstrated the significant potential of the developed photocatalytic system for the treatment of waste effluent generated by the palm oil industry and other agro-industries, with the ability to simultaneously reduce a number of organic pollution indicators (OPIs).

  • Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning
    • Andrzej Czyżewski
    2023 Full text Journal of the Acoustical Society of America

    Text-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the context of ASR, can be used to optimize a model based on specific goals. A model can be trained to minimize errors in speech-to-text transcription, especially for technical medical terminology. In this case, the "reward" to the RL model can be negatively proportional to the number of transcription errors. The paper presents a method and experimental study from which it is concluded that the combination of TTS and RL can enable the creation of a speech recognition model that is better suited to the specific needs of medical personnel, helping to expand the training data and optimize the model to minimize transcription errors. The learning process used reward functions based on Mean Opinion Score (MOS), a subjective metric for assessing speech quality, and Word Error Rate (WER), which evaluates the quality of speech-to-text transcription.

  • Optimum Choice of Randomly Oriented Carbon Nanotube Networks for UV-Assisted Gas Sensing Applications
    • Katarzyna Drozdowska
    • Adil Rehman
    • Janusz Smulko
    • Aleksandra Krajewska
    • Bartlomiej Stonio
    • Pavlo Sai
    • Aleksandra Przewłoka
    • Maciej Filipiak
    • Krystian Pavłov
    • Grzegorz Cywinski
    • Dmitry V. Lyubchenko
    • Sergey Rumyantsev
    2023 Full text ACS Sensors

    We investigated the noise and photoresponse characteristics of various optical transparencies of nanotube networks to identify an optimal randomly oriented network of carbon nanotube (CNT)-based devices for UV-assisted gas sensing applications. Our investigation reveals that all of the studied devices demonstrate negative photoconductivity upon exposure to UV light. Our studies confirm the effect of UV irradiation on the electrical properties of CNT networks and the increased photoresponse with decreasing UV light wavelength. We also extend our analysis to explore the lowfrequency noise properties of different nanotube network transparencies. Our findings indicate that devices with higher nanotube network transparencies exhibit lower noise levels. We conduct additional measurements of noise and resistance in an ethanol and acetone gas environment, demonstrating the high sensitivity of higher-transparent (lower-density) nanotube networks. Overall, our results indicate that lower-density nanotube networks hold significant promise as a viable choice for UV-assisted gas sensing applications.

  • Optimum number of actuators to minimize the cross-sectional area of prestressable cable and truss structures
    • Ahmed Manguri
    • Najmadeen Saeed
    • Farzin Kazemi
    • Marcin Szczepański
    • Robert Jankowski
    2023 Structures

    This paper describes a new computational method for determining the optimum number of actuators to design the optimal and economic cross-sectional area of pin-jointed assemblies based on the conventional force method. The most active members are selected to be prestressed to redistribute stress in the whole structure, resulting in regulating the internal force of bars that face high stress. Reducing stress in critical members allows the designers to select smaller cross-sectional areas than before. Furthermore, the maximum absolute displacement of the structures before the optimization is set as a limit for the displacement of the optimized structures. The derived equations from the force method are subjected to the optimization algorithms (i.e., sequential quadratic programming (SQP), trust-region reflective, active set, and interior point) to minimize the necessary number of actuators for prestressing. The optimization procedure is done in two ways: first, by minimizing the number of actuators for prestressing through implementing the fmincon function, and second, by selecting the most economical area via prestressing the structure before loading. The method is applied to the numerical models of two cable and four truss structures that were previously studied. The outcomes show that by actuating as few actuators as possible, the area of cable and truss structures can be minimized up to 17% and 27 %, respectively. Moreover, 5% improvement can be obtained applying the current technique to the optimized trusses by quoted methods. The outcomes are compared with results from the literature. Moreover, the results obtained from MATLAB are verified by SAP2000 software.

  • Optimum shapes and dimensions of rubber bumpers in order to reduce structural pounding during seismic excitations
    • Seyed Mohammad Khatami
    • Hosein Naderpour
    • Alireza Mortezaei
    • M. Maddah
    • Natalia Lasowicz
    • Robert Jankowski
    2023 Full text Structures

    Large displacement of structures observed during seismic excitations may lead to collisions between two adjacent, insufficiently-separated buildings and may result in major damages of both of them. In many building codes, appropriate equations or approximately recommended distances between structures in order to avoid pounding hazard have been introduced. Unfortunately, further, more detailed considerations show that safety situation or economic aspects are not always satisfied due to the collisions between buildings and the cost of land, respectively. Hence, researchers have studied other approaches of reducing the negative pounding effects. Such methods include the use of tuned mass or liquid dampers. Moreover the increase in stiffness of building or reduction of mass of the structure are still considered. In this paper, another approach is considered by the application of rubber bumpers placed between buildings. The bumpers are attached at each story to absorb energy during impact. Several different shapes and dimensions of bumper elements were numerically investigated so as to find the most effective ones most effective in reducing structural pounding negative effects. For this purpose, two MDOF models of 3-story and 4-story buildings were firstly considered. Such parameters as lateral displacement, damage index, dissipated energy and impact forces were calculated and depicted as the results of numerical study. Then, different shapes and dimensions of bumpers were parametrically investigated.