In the context of climate change, environmental actions on structures are likely to alter in terms of intensities and frequencies of occurrence. To ensure sufficient load-bearing capacity of structures despite these changes, actions may be monitored using structural health monitoring (SHM) systems. Environmental actions involve time-dependent and non-scheduled loads, e.g., wind and snow loads. In current SHM systems, these loads are mostly traced locally. However, local monitoring may cause inaccuracies, as certain load phenomena, such as wind turbulences, or snow accumulations in specific parts of structures, may not be registered. A holistic, global recording of loads acting on structures has rarely been established since a multitude of sensors is cost intensive, and the integration into the building envelope is challenging. This paper investigates slender layered piezoresistive sensors to measure loads resulting from environmental actions, focusing on wind and snow loads. The sensors operate based on changes of externally applied pressure, leading to variations in the electrical resistance of a piezoresistive material. Next to strategies for quantifying structural loads using sensor technology, first, alternatives of force sensors are discussed. Subsequently, the low-cost technical fabrication of the piezoresistive pressure sensors is presented, and implementation, calibration, and validation of the pressure sensors are conducted. Finally, the validation results of the sensors are discussed, and an outlook on future work is presented. In summary, the sensors investigated offer a wide range of applications for monitoring structural actions on surfaces, serving as a basis for estimating the load-bearing capacity of structures reliably.
Isogeometric analysis (IGA) in the framework of the boundary element method (BEM) – known as isogeometric boundary element analysis or IGABEM – has shown recently respectable performance in the field of acoustics and handling the time harmonic wave propagation equation of Helmholtz. However, IGABEM still requires fine meshes to handle the cases of very high frequencies due to the large number of elements needed to capture the oscillatory behaviour, leading to large computational costs. IGABEM can be enriched by the partition of unity expansion of plane waves in the framework of the eXtended IGABEM (XIBEM) which can be used to simulate high frequency problems with small-scale wavelengths using coarser meshes than those used in other numerical approaches. In this paper, two numerical investigations are performed using XIBEM for two-dimensional problems. First, the number of plane waves is varied to find out the suitable enrichment scheme to achieve accurate results for higher frequency problems than those in the literature. After that, XIBEM is coupled with a non-iterative topological-shape sensitivity inverse analysis and applied for the problem of scatterer shape reconstruction. Different distributions of the receptor points are checked for this investigation with varied initial scatterer shapes.
The structural and electronic properties of XC7 (X = B, Al, N, P, Ge) sheets were investigated through first-principles calculations. Two types of graphene-like sheets named g- and β-XC7 with honeycomb lattice structures were considered. Their cohesive energies indicate that these sheets are energetically favorable. Both g- and β-XC7 sheets were found to have good stabilities. The results indicate that g-GeC7 and β-GeC7 sheets, as well as g-SiC7 and β-SiC7 sheets, are semiconductors. Their band gaps are dependent on the arrangement of Ge and C atoms. For X = B, Al, N, and P, the g- and β-XC7 sheets are predicted to have metallic properties. Our results show that graphene-like XC7 sheets with proper electronic properties may be good candidates for applications in modern and future nanoelectronic devices.
The alkali-silica reaction (ASR) starts with the dissolution of silica in alkali-reactive aggregates. Besides the thermodynamic basics of silica dissolution, kinetic data are needed for a modelling of the ASR to describe the temporal course of the reaction. Although the dissolution kinetics of silica has been investigated frequently, data for concrete-like environments (pH > 12) and under special influences as NaCl (de-icer impact) or Al (released from feldspars) are rare or missing. In the present study, long-term (2.5 years) dissolution tests were performed to obtain kinetic data for two types of aggregate (granodiorite, rhyolite) under concrete-like conditions (pH 13.8, with/without Ca(OH) 2 and NaCl) at temperatures of 25, 45 and 60 °C. The findings show that silica dissolution rates effective for ASR differ up to four orders of magnitude, ranging from (log r 1 ) −13.13 to −9.96 mol/m²s as a function of aggregate type, temperature, NaCl concentration and Ca(OH) 2 . Besides a predominant influence of the temperature and Ca(OH) 2 , NaCl promotes the dissolution of silica and lowers the Al concentration, especially at higher temperatures. The activation energy for the silica dissolution (84±4 kJ/mol) has not changed significantly in presence of NaCl.
The free overfall is a simple and widely used device for measuring discharge in open irrigation channels and agricultural research projects. However, the direct measurement of discharge can be difficult and time consuming with care needed to minimize potential inaccuracies of empirical equations applied to site-specific conditions. Thus, in the present study four standalone algorithms of Isotonic Regression (ISO), Least Median of Square Regression (LMS), M5Prime (M5P) and REPT and four novel hybrid algorithms of rotation forest (ROF) combined with those four standalone models (i.e. ROF-ISO, ROF-LMS, ROF-M5P and ROF-REPT) were applied for the intelligent prediction of discharge per unit width for the free overfall condition in rectangular channels. This was accomplished via six data sets (355 data) collected from published literature including end-depth, Manning's roughness coefficient, channel width, bed slope and unit discharge. The dataset was partitioned in a 70:30 ratio randomly, 70% (248 data) of data used for model development while 30% (107 data) applied for model validation. Also, four different input combinations were constructed to identify the most effective prediction method. Furthermore, results were validated using several visually-based (line graph, scatter plot, violin plot and Taylor diagram) and quantitative-based [Root mean square error (RMSE), Nash-Sutcliffe Efficiency (NSE), Willmott’s index of agreement, Legates and McCabe coefficient of efficiency (LM)] approaches. Results of the sensitivity analysis revealed that end-depth had the highest effect on the results, while channel width was least influential. Results also showed that the best input combination incorporated all four input parameters. According to the results, ROF-REPT had the best performance with RMSE of 0.0035 (m3/s/m), NSE of 0.990, WI of 0.997% and LM of 0.905% followed by ROF-M5P REPT, M5P, ROF-LMS, ISO and LMS.
Recent developments in smart sensing technologies have fostered the wide-spread utilization of smart city applications, which rely on Internet of Things (IoT) frameworks to work efficiently. The terms “smart city” and “IoT framework”, however, have been given several definitions, without consensus. Consequently, definitions of the terms “smart city” and “IoT framework” need to be condensed, consolidating concepts and guidelines of smart cities and IoT frameworks, as will be shown in this study. In addition, a systematic survey of IoT frameworks for smart city applications is presented, summarizing and comparing the technologies and architectures of IoT frameworks for smart city applications. As a result of this study, trends in IoT frameworks for smart city applications and a definition of the term “smart city” are provided. Materializing the findings achieved in this study, an abstract IoT framework concept for smart city applications is proposed. It is expected that the definition of the term “smart city” may be used as a basis for a generally accepted formal definition and that the proposed IoT framework concept may provide a strong foundation for successful IoT framework implementations in the context of smart city applications.
The applications of steel slag (SS) in the cement industry provide a sustainable solution both for construction materials and industrial solid wastes, but the problem of easily bleeding presents a threat to its later performance and utilization efficiency. This work was designed to improve the water retention of SS blended cement by using hydroxyethyl methyl cellulose (HEMC) and evaluate the physio-chemical properties of the modified SS cement. The interaction mechanism between HEMC and SS blended cement was explored via calorimetry, acoustic and electroacoustic spectrometer, low-field NMR relaxometry, XRD, TG-DSC, as well as fluorescent microscopy. Results show that the water retention of SS blended cement mortar is improved both by ultra-fine grinding and incorporating HEMC, while the water retention rates are prominently increased from 84.87% to 94.35% by 0.1% HEMC. Ultra-fine grinding is able to activate SS and improve water retention ability more efficient compared with the application of HEMC. HEMC does not modify the amount of hydration heat but postpones the occurrence of the exothermic peak. A sudden increase in zeta potential demonstrates the Ca²⁺-adsorption on surfaces of SCM and cement particles. An additional peak commences on the ¹H NMR spectrum of HEMC incorporated cement paste, which reflects the water adsorbed by HEMC, starting to submerge after 500 min via the desorption of water. HEMC affects the SS blended mortar via adsorbing on particles in the paste, capturing water and Ca²⁺ around it, thus improving the water retention and retarding the hydrate-polymerization.
Die kühlende Wirkung von Fassadenbegrünung wird in der Literatur häufig als ein Ansatz zur Bekämpfung des Klimawandels und der erhöhten Temperaturen im Sommer diskutiert. Neben diesem Effekt können Fassadenbegrünungen auch im Winter eine Dämmwirkung entfalten. In dieser Studie wurden die Auswirkungen eines Fassadenbegrünungsmoduls auf den Wärmetransport durch die dahinter stehende Außenwand während der Heizperiode untersucht. Dafür wurden empirische Messungen an einem Prototyp eines Begrünungsmoduls an einem Testcontainer durchgeführt. Zu den gemessenen Parametern gehörten die Oberflächen‐ und Lufttemperatur (jeweils innen und außen) und die Wärmestromdichte durch den Wandaufbau. Die Messungen wurden an zwei verschiedenen Stellen durchgeführt: ohne Begrünung (Referenzwand) und mit Begrünung. Der Wärmedurchgangskoeffizient der Fassade wurde für beide Stellen nach drei verschiedenen Datenfilterungsmethoden berechnet. Die Ergebnisse zeigen, dass das Fassadenbegrünungsmodul den Wärmetransport durch die Wand reduziert. Je nach verwendeter Filterungsmethode ergibt sich durch die Fassadenbegrünung eine Reduktion des Wärmedurchgangskoeffizienten des Wandaufbaus um 9 % bis 18 %.
In 2019 und 2020 wurde die Balkanregion von zwei Erdbeben der Stärke MW = 6,4 heimgesucht. Am 26. November 2019 erschütterte ein Erdbeben den Nordwesten Albaniens. Es war das stärkste Erdbeben seit mehr als 40 Jahren. Städte wie Thumanë, Tirana und Durrës erlitten Schäden, wobei Durrës mit mehreren eingestürzten Gebäuden am stärksten betroffen war. Die Region Sisak‐Moslavina in Kroatien, etwa 50 km südlich von Zagreb, wurde am 29. Dezember 2020 erschüttert. Auch hier war es das stärkste Erdbeben seit dem Pokupsko‐Erdbeben vom 8. Oktober 1909 und das größte Erdbeben in der Region seit 140 Jahren. Es verursachte umfangreiche Schäden in den Städten Petrinja, Glina und Sisak sowie in zahlreichen benachbarten Kleinstädten und kleinen Siedlungen der Region. Die Schüttereffekte und Gebäudeschäden beider Erdbeben konnten im Rahmen von Feldeinsätzen untersucht und dokumentiert werden. In Durrës (Albanien) verursachte das Erdbeben erhebliche Schäden an Stahlbetonskelettbauten mit Ausfachungswänden. Schäden infolge des Erdbebens in Kroatien konzentrieren sich hingegen auf ältere und moderne Gebäude aus unbewehrtem Mauerwerk. Der Beitrag gibt einen Überblick über die erdbebeninduzierten Schäden in verschiedenen Gebäudetypen und deren Variationen. Die Ursachen der Schäden sowie die Konsequenzen für die schnelle Reaktion auf ein Erdbeben werden in engem Zusammenhang mit der Normung in moderat erdbebengefährdeten Gebieten in Europa diskutiert. The Magnitude 6.4 – earthquakes in Albania and Croatia – engineering analysis of earthquake damage and lessons for standardization in Europe In 2019 and 2020, the Balkan region was hit by two earthquakes of magnitude MW = 6.4. On November 26, 2019, an earthquake struck Northwestern Albania. It was the strongest to hit Albania in more than 40 years. Cities such as Thumanë, Tirana, and Durrës suffered damage, but Durrës was the hardest hit with several building collapsed. On December 29, 2020, an earthquake occurred in the Sisak‐Moslavina county of Croatia, located approximately 50 km south of Zagreb. Comparable to Albania, it was the strongest to hit in the Pokupsko‐Petrinja seismic zone since the October 8, 1909 Pokupsko earthquake and the largest earthquake in the region in 140 years. It caused extensive damage in the cities of Petrinja, Glina, and Sisak as well as in numerous neighboring small towns and small settlements in the region. The shaking effects and building damage of both earthquakes could be investigated and documented during field operations. In Durrës (Albania), the most affected buildings by the earthquake damage were reinforced concrete (RC) frame buildings with infill walls. Whereas, the Croatia earthquake caused major damage on the older and modern unreinforced masonry buildings. The paper provides an overview of the earthquake‐induced damage in different types of buildings and their variations. The causes of the damage as well as the consequences for rapid response to earthquake are discussed in close relation to the standardization in low to moderate seismic regions in Europe.
An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple model (IMM) to predict SSL. Telar River branches into two main branches: Telar and Kasilian. The modeling process consisted of two levels: 1) creating hybrid models and 2) creating ensemble models. At the first level, the Honeybadger optimization algorithm (HBOA), salp swarm algorithm (SSA), and particle swarm optimization (PSO) were applied to set the parameters of the radial basis function neural network (RBFNN) models. The IMM model was used to integrate the outputs of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models into the RBFNN model at the second level. Inputs to the models included lagged rainfall, discharge, and SSL. Several new ideas have been introduced in the current paper, including hybrid RBFNN models, a gamma test for selecting optimal input combinations, an analysis of output uncertainty, and an advanced IMM for SSL prediction. Various performance evaluation criteria, including root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), and percentage bias (PBIAS), were used to evaluate the models. The comparative results indicated high accuracy of IMM with an MAE of 0.983, NSE of 0.254, PBIAS of 0.991 at Telar station. The training MAE of the IMM model was 4.4%, 4.8%, 6.7%, 52%, and 9.2% lower than that of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models at Kasilian station. The study results revealed that the IMM and RBFNN-HBOA provided lower uncertainty than the other RBFNN models. Thus, the IMM model represents the most accurate estimation of SSL.
Accurate examination of electricity generation stemming from higher-order deformation (flexoelectricity) in 2D layered materials is a highly challenging task to be investigated with either conventional computational or experimental tools. To address this challenge herein an innovative and computationally efficient approach on the basis of density functional theory (DFT) and machinelearning interatomic potentials (MLIPs) with incorporated long-range interactions to accurately investigate the flexoelectric energy conversion in 2D van der Waals (vdW) bilayers is proposed. In this approach, short-range interactions are accurately defined using the moment tensor potentials trained over computationally inexpensive DFT-based datasets. The long-range electrostatic (charge and dipole) and vdW interaction parameters are calibrated from DFT simulations. Elaborated comparison of mechanical and piezoelectric properties extracted from the herein proposed approach with available data confirms the accuracy of the devised computational strategy. It is shown that the bilayer transition metal dichalcogenides can show a flexoelectric coefficient 2–7 times larger than their monolayer counterparts. Noticeably, this enhancement reaches up to 20 times for Janus diamane and fluorinated boron-nitrogen derivatives of diamane bilayers. The presented results improve the understanding of the flexoelectric effect in vdW heterostructures and moreover the proposed MLIPbased methodology offers a robust tool to improve the design of novel energy harvesting devices.
This review discusses various grades of titanium biomaterials and their sustainable grindability for application in the medical field. Titanium biomaterials are most commonly utilized for medical applications due to their exceptional characteristics such as high corrosion resistance and biocompatibility. The presented review looks at the principal requirements of titanium for medical applications, such as some good mechanical properties, biocompatibility, corrosion, wear resistance properties, and processability that facilitate the successful implantation of implants. It discusses the various types of titanium alloys that are commercially available and, more specifically, used for medical applications. It highlights the properties of different grades of titanium alloys and further narrows down its primary focus on applications, advantages, and shortcomings of commercially available titanium biomaterials. Machining titanium alloys is a difficult task due to their inherent properties such as low thermal conductivity and chemical reactivity at high temperatures and usually results in changes in metallurgy and surface integrity at the machined surface. Conventional machining, which has been the main machining method, has some limitations related to environmental hazards, cutting fluid costs, and operator health issues that have necessitated the development of sustainable machining. The emphasis in this review has been placed on sustainable grinding techniques such as MQL machining, cryogenic machining, nano-particle MQL machining, high-pressure machining, and solid lubrication machining used to grind titanium alloys and their benefits and limitations. Finally, the review will highlight some of the potential areas for future research and trends on different cooling and lubrication methods in the sustainable grinding of titanium alloys for medical applications. It is believed that this review will be of great benefit to the industries involved in manufacturing titanium-based medical implants.
This study presents the application of the peridynamic differential operator (PDDO) on modeling of bi-material plates with/without modulus graded regions. The PDDO converts the Navier’s equilibrium equations and boundary conditions from the differential form into the integral form. The mismatch of the stiffness along the interface of two distinct materials results in an increase in the strain and stress variations, leading to the onset of cracking at the free corners of the interface. The interfacial strains and stresses can be mitigated by inserting a modulus graded layer between two different materials. The material properties in the modulus graded region is achieved through the power-law distribution. The efficacy of the proposed approach is demonstrated by considering a bi-material square plate under tension. The PDDO displacement, strain, and stress predictions are compared with the reference solutions, and good correlations are achieved. The influence of a modulus graded region with/without a pre-existing crack located between dissimilar materials is investigated for different material variations. It is noted that the PDDO performs very well on the displacement, strain, and stress predictions even if the solution domain has geometrical or material discontinuities. Moreover, modulus graded regions offer some advantages over the sharp interfaces and alleviate the strain and stress concentrations along the interface of the dissimilar materials.
Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran’s Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables.
This study followed the approach of dispersing and localizing carbon nanotubes (CNTs) in nanostructured domains of block copolymers (BCPs) by shortening the CNTs via ball milling. The aim was to selectively tune the electrical and mechanical properties of the resulting nanocomposites, e.g., for use as sensor materials. Multiwalled carbon nanotubes (MWCNTs) were ground into different size fractions. The MWCNT length distribution was evaluated via transmission electron microscopy and dynamic light scattering. The nanostructure of the BCPs and the glass transition temperatures of the PB-rich and PS phases were not strongly affected by the addition of CNTs up to 2 wt%. However, AFM and TEM investigations indicated a partial localization of the shortened CNTs in the soft PB-rich phase or at the interface of the PB-rich and PS phase, respectively. The stress-strain behavior of the solution-mixed composites differed little from the mechanical property profile of the neat BCP and was largely independent of CNT amount and CNT size fraction. Significant changes could only be observed for Young’s modulus and strain at break and may be attributed to CNT localization and small changes in morphology. For nanocomposites with unmilled CNTs, the electrical percolation threshold was less than 0.1 wt%. As the CNTs were shortened, the resistivity increased and the percolation threshold shifted to higher CNT contents. Composites with CNTs ground for 7.5 h and 13.5 h showed no bulk conductivity but significantly decreased surface resistivity on the bottom side of the films, which could be attributed to a sedimentation process of the grind and thereby highly compressed CNT agglomerates during evaporation.
Gels are a mixture of cross-linked polymers and solvents, and have been widely studied in recent years for a diverse range of biomedical applications. Because gels can undergo large, reversible shape changes due to swelling, their complex physical response must be modeled by coupling large reversible deformation and mass transport. An ongoing challenge in this field is the ability to capture swelling or residual swelling-induced of such stimuli-responsive gels from initially flat two-dimensional (2D) to three-dimensional (3D) curved shapes. Specifically, because such shape changes typically involve large deformations, shape changes, and the exploitation of elastic instabilities, it remains an open question as to what external stimulus should be prescribed to generate a specific target shape. Therefore, we propose a novel formulation that tackles, using both nonlinear kinematics and material models, the coupling between elasticity and solvent transport using Kirchhoff–Love shell theory discretized using isogeometric analysis (IGA). Second, we propose an inverse methodology that chemomechanically couples large deformation and mass transport to identify the external stimuli prescribed to generate a specific target shape. Our numerical examples demonstrate the capability of identifying the required external stimuli, with the implication that the reconstructed target shapes are accurate, including cases where the shape changes due to swelling involve elastic instabilities or softening. Overall, our study can be used to effectively predict and control the large morphological changes of an important class of stimuli-responsive materials.
A one-day Nordic Concrete Research workshop on “Accelerated freeze-thaw testing of concrete” attracted approx. 30 participants. The workshop included presentations on various aspects, such as observed frost damage in the field and the importance of the temperature curve during testing as well as other interactions with the surroundings of the concrete. The workshop also included examples of recent research, which can improve our knowledge about the frost damage mechanism and therefore provide input to improving the standardised test methods. The present paper is a summary of the nine presentations and the discussion arising from the presentations.
This study combines 2D element mappings obtained by laser ablation with inductively coupled plasma mass spectrometry (LA-ICP-MS) and energy dispersive X-ray spectrometry (EDX) in the scanning electron microscope (SEM) to analyse the chemical composition of cement clinker phases. It is revealed that this approach enables to determine the major and trace element concentrations in phases like alite, belite and the interstitial phase of real Portland cement clinker. A protocol is shown how to record and subsequently register both datasets as such, that the combined analysis significantly broadens the output of the individual measurements. The low detection limits of LA-ICP-MS delivers trace element concentrations and the high spatial resolution and analytical accuracy of SEM-EDX identifies the clinker phases. Results show that Ba, K, V, and Rb are preferentially incorporated into belite. This allows to study the influence of minor and trace elements on the stabilisation and reactivity of clinker phases.
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