Nonlinear analysis of concrete-filled steel square tube strengthened by internal transverse stiffened bars under axial compression

A new type of concrete-filled square steel tubular(CFSST) column is proposed, which is characterized by the transverse stiffened steel bars arranged inside the pipe wall to improve confinement performance of the concrete core. This paper employs a nonlinear analysis of square CFSST stub columns under axial compression. A three-dimensional nonlinear finite element (FE) model is developed using ABAQUS, where nonlinear material behavior and enhanced strength corner properties of steel are included. Close agreement is achieved between the test and FE results in terms of load-deformation response and ultimate strength

The experimental research on axial compression performance of concrete-filled steel square tube strengthened by internal transverse stiffened bars

The application of Concrete-filled square steel tubular column in structural engineering is more and more widely. In order to improve the confinement of concrete core, the configuration of concrete-filled square steel tube strengthened by internal transverse stiffened bars is proposed. This paper employs a experimental research on concrete-filled square steel tube strengthened by internal transverse stiffened bars under axial compression and the influencing factors of bearing capacity such as diameter and spacing of steel bars is analyzed. The results shows that its axial load capacity is increased 2.3%~13.1% than that of ordinary concrete-filled square steel tubular column, spacing changes to improve the bearing capacity of concrete filled square steel tubular column strengthened by transverse stiffened bars is more effective than the increase in stiffened bars diameter

Detektion von anormalen Zeitreihen an Persistent-Scatterer-Punkten im Zusammenhang mit der Ableitung flächenhafter Bodenbewegungen

Zur Erfassung von Bodenbewegungen hat sich die radarinterferometrische Methode der Per-sistent Scatterer Interferometrie (PSI) aufgrund der erreichbaren hohen Qualität und der zu-nehmenden Verfügbarkeit von Radar-Satellitendaten in vielen Bereichen der Wissenschaft, Wirtschaft und Behörden bewährt. Im Vergleich zu herkömmlichen Vermessungsverfahren er-zeugt die PSI-Methode eine hohe Punktdichte mit Informationen über die zeitlich variierenden Bodenbewegungen bzw. Höhenänderungen an der Tagesoberfläche und mit einer Genauig-keit im Sub-Zentimeterbereich. Aufgrund verschiedener, auch verfahrenstechnischer Ursa-chen können die ermittelten PSI-Höhenänderungszeitreihen an einzelnen PS-Punkten jedoch von dem erwarteten flächenhaften Bodenbewegungsverhalten abweichen. Sie sind somit für dieses Bodenbewegungsverhalten nicht repräsentativ oder im messtechnischen Sinne grob falsch. Vor einer weiteren Nutzung dieser Ergebnisse sollten diese auffallenden „Anoma-lien“ deshalb in den vorliegenden Massendaten automatisiert erkannt und von einer Interpre-tation des Bodenbewegungsverhaltens ausgeschlossen werden. Im Rahmen der vorliegenden Arbeit wird ein raumzeitliches Clusterverfahren vorgestellt, das die automatisierte Detektion von anormalen Zeitreihen an PS-Punkten in flächenhaft vorlie-genden PSI-Ergebnissen ermöglicht. Zur Auswahl eines für diese Aufgabenstellung geeigne-ten Verfahrens wurden verschiedene Clusterverfahren auf ihre Anwendbarkeit hin untersucht und letztlich das Verfahren „Local-Moran’s-Index“ auf Grundlage lokaler räumlicher Autokorrelationen ausgewählter Attribute zwischen den PS-Punkten an praktischen Beispielen getestet. Es zeigte sich, dass grundsätzlich mit Hilfe des Local-Moran’s-Index - Clusterverfahrens signifikante „räumliche Anomalien“ detektiert werden können, dabei aber der Wahl des Attributes eine wichtige Bedeutung zukommt. Im Vergleich zur Clusteranalyse mit den Attributen „Polynomgrad“ und „Höhenänderung“ (abgeleitet aus einer Trendpolynombestimmung) konnte die Clusteranalyse mit dem Attribut „Höhenänderungsrate" ein deutlich besseres Ergebnis erzielen. Darüber hinaus ergab die Untersuchung, dass zur Beschreibung des charakteristischen Verlaufs einer Höhenänderungszeitreihe ein einziges Attribut offensichtlich nicht ausreicht. Da bei der Local-Moran’s-Index - Clusteranalyse jedoch nur ein einziges Attribut berücksichtigt werden kann, erfolgt eine Modifikation dieses Verfahrens durch Einführung eines Gewichtungsfaktors zur Beurteilung und Quantifizierung der Ähnlichkeit der Höhenänderungsverläufe benachbarter PS-Punkte. Des Weiteren wurde ein Kriterium abgeleitet, das auf einer gemeinsamen Betrachtung von räumlichen Autokorrelationen und der Anzahl benachbarter PS-Punkte basiert, und zu einer Modifikation der Local-Moran’s-Index - Clusteranalyse führte. Erweitert wurde das Vorgehen um eine kleinräumige Clusteranalyse mit spezifischen, angepassten Parametern. Abschließend wurde am Beispiel eines umfangreichen Testdatensatzes das erweiterte und modifizierte Local-Moran’s-Index - Verfahren zur Identifizierung von anormalen Zeitreihen an PS-Punkten erprobt und das Analyseergebnis mit einem entwickelten statistischen Validati-onsverfahren, basierend auf berechneten Höhenänderungsdifferenzen, überprüft. Zusammen-fassend zeigte sich, dass eine automatisierte Detektion von anormalen Zeitreihen an massen-haft vorhandenen PS-Punkten mittels des modifizierten Local-Moran’s-Index - Verfahrens zu-verlässig erfolgen kann. Die hierdurch ermöglichte Datenbereinigung liefert einen wichtigen Beitrag zur Verbesserung der Qualität einer flächenhaften Modellierung von Bodenbewegun-gen.Due to the achievable high quality and the increasing availability of radar satellite data, the radar interferometric method of persistent scatterer Interferometry (PSI) has proved of great value for the detection of ground movements in many fields of science, economics and public authorities. Compared to conventional surveying methods, the PSI method produces a high point density with information about the temporally varying ground movements or height changes on the ground surface with an accuracy in the sub-centimeter range. However, due to various, also procedural reasons, the determined PSI time series of height changes at individual PS-points can deviate from the expected areal ground movement behavior. They are therefore not representative to describe this ground movement behavior or are grossly wrong in the metrological sense. Before any further use of these results, these striking "anomalies" should therefore be recognized automatically in the present mass data and excluded from an interpretation of the ground movement behavior. In the present work, a space-time clustering method is presented, which enables the automated detection of abnormal time series at PS points in areal PSI results. In order to select a suitable method for this task, various clustering methods have been investigated for their applicability and finally the "Local-Moran's-Index" method on the basis of local spatial autocorrelation of selected attributes between the PS points has been tested in practical examples. It was found that in principle significant “spatial anomalies” can be detected with the help of the Local-Moran's-Index clustering method, but the choice of attribute is of great importance. In comparison to the cluster analysis with the attributes "polynomial degree" and "height change" (derived from a trend polynomial determination), the cluster analysis with the attribute "height change rate" was able to achieve a significantly better result. Furthermore, the investigation showed that a single attribute is obviously not sufficient to describe the characteristic course of a height change time series. However, since only a single attribute can be taken into account in the Local-Moran's-Index cluster analysis, a modification of this method is carried out by introducing a weighting factor for judging and quantifying the similarity of the height changes of adjacent PS points. Moreover, a criterion was derived based on a joint consideration of spatial autocorrelation and the number of neighboring PS points and led to a modification of the Local-Moran's-Index cluster analysis. The approach was then extended by a small-scale cluster analysis with specific, adapted parameters. Finally, using the example of a comprehensive test data set, the extended and modified Local-Moran's Index method for the identification of abnormal time series at PS points was tested, and the analysis result was checked with a developed statistical validation method based on calculated differences of height changes. In summary, it was found that automated detection of abnormal time series at mass PS points can be reliably performed by means of the modified Local-Moran's-Index method. The data cleansing provided thereby makes an important contribution to the improvement of the quality of areal modeling of ground movements

Development and Application of a Hydrogeochemical Model for the Groundwater Treatment Process in Waterworks

Drinking water quality is one of the most important factors affecting human health. The task of the waterworks is to purify raw water into drinking water. The quality of drinking water depends on two major factors: the raw water quality, and the treatment measures that are applied in the waterworks. Since the raw water quality develops over time, it must be determined whether the treatment measures currently used are also suitable when the raw water quality changes. For this reason, a hydrogeochemical model relevant to the drinking water quality during the treatment process was developed. By comparing the modeled results with the measured values, with the exception of chloride and sodium, all other relevant water quality parameters were consistent with one another. Therefore, the model proved to be plausible. This was also supported by the results of mass balance. The model can be used to forecast the development of drinking water quality, and can be applied as a tool to optimize the treatment measures if the raw water conditions change in the future

Spatio-Temporal Pattern and Conflict Identification of Production–Living–Ecological Space in the Yellow River Basin

Production–living–ecological space (PLES) is the main body of the optimization of the development and protection pattern of territorial space, and the spatial conflict in PLES reflects a struggle for ecological protection and socio-economic development in the process of spatial development and utilization. The Yellow River Basin is one of the most concentrated and prominent areas of spatial conflict of PLES in China. Therefore, clarifying the spatio-temporal pattern of PLES of the region and scientifically identifying the characteristics of its spatial conflict will significantly improve the efficiency of comprehensive utilization of spatial resources, promote the integrated and orderly development of resource elements in the basin, and eventually achieve the strategic goals of ecological protection and high-quality development of the Yellow River Basin. In this research, the CA–Markov model was applied to simulate the spatio-temporal pattern of PLES in the Yellow River Basin from 2010 to 2025, and the landscape ecology method was adopted to construct the spatial conflict of the PLES measurement model for identifying the spatio-temporal trends of conflicts and their intensity. The results reveal that, from 2010 to 2025, ecological–production space (EPS) dominates the PLES in the Yellow River Basin, as its total area remains stable amid fluctuations; living–production space (LPS) shows the most notable change, as it grows yearly along with urbanization and industrialization process of the region; the transition between ecological–production space (EPS) and production–ecological space (PES) is the most frequent, and the two also account for the largest area. Spatial conflict of PLES in the Yellow River Basin is mainly reflected in the encroachment of LPS on other PLES, concentrated in the regions from Hekou Town to the left bank of Longmen, Fen River, Shizuishan to the southern bank of Hekou Town, and Daxia River and Tao River in the Yellow River Basin. From 2010 to 2025, the space conflict composite index of PLES (SCCI) of most regions in the basin lies within 0.7, which is a stable or basically controllable level. Among the 29 tertiary water resource divisions in the Yellow River Basin, the SCCI of 15 indicate a major, decreasing trend

A Comparison of Research Methods to Determine the Sustainability of Mineral Resources in Henan Province Based on Cloud Analysis

In order to improve the fuzzy comprehensive evaluation model of mineral resource sustainability and enhance its scientific and objective nature, in this paper, a cloud model-based risk assessment method is introduced to determine the sustainability of mineral resources in a comprehensive comparison, while using a combination of subjective and objective weighting method combining improved hierarchical analysis and the entropy weighting method. Compared with the previous single-assignment evaluation method, the method used in this paper has the advantages of more reasonable determination of weights, more accurate results and better visualization. On this basis, the combined weight method, cloud model method and hierarchical fuzzy evaluation method are organically combined to conduct a comprehensive evaluation of the sustainability of mineral resources in Henan Province. The case analysis shows that the comprehensive evaluation results of the sustainability of mineral resources obtained according to the method are scientifically reasonable and have important reference value and promotion significance for quantitative research in related fields

Dynamic Tensile Mechanical Properties of Outburst Coal Considering Bedding Effect and Evolution Characteristics of Strain Energy Density

The evolution of strain energy density of outburst-prone coal is of great significance for analyzing the characteristics of energy accumulation and release in coal and rock masses. The dynamic mechanical properties of coal samples were tested by using the split Hopkinson pressure bar (SHPB) technique. Dynamic tensile mechanical properties, layered effect and density evolution characteristics of strain energy for coal were studied. The dynamic failure and crack propagation process of the specimen were recorded with a high-speed camera. In addition, a digital image correlation (DIC) method was used to analyze the evolution characteristics of the strain field during the deformation process of the specimen. The distribution characteristics of the particle fragments were statistically analyzed. The results show that the bedding orientation of the coal has a significant effect on its deformation and damage features. The presence of weak planes, microcracks and laminae causes its shear damage zone to behave more complex. If the crack plane coincides with the high shear stress plane, the developed shear cracks extend along the weak laminae and the shear damage zones in BD specimens are not symmetrically distributed. When the laminated surface of the coal sample is at a certain angle with the impact loading direction, the damage mode is coupled with tensile and shear damage. The percentage mass distribution of particles and fines increases with increasing bedding orientation. The effect of water on the dynamic damage of coal samples is significant. Based on the principle of pressure expansion of wing-shaped cracks, the formula for calculating the dynamic strength of water-saturated coal samples under dynamic loading was derived