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Die Bemessung von tragendem Lehmmauerwerk erfolgt in Deutschland auf Basis der Lehmbau Regeln. Das dort verankerte Nachweisverfahren beruht auf einem globalen Sicherheitskonzept, welches aus zuverlässigkeitstheoretischer Sicht nicht mehr dem Stand der Technik entspricht. Auf Grund dessen wird das Bemessungskonzept für Lehmmauerwerk vom Deutschen Institut für Bautechnik (DIBt) voraussichtlich 2023 außer Kraft gesetzt. Ein statischer Nachweis von tragenden Lehmbauten wäre in Deutschland dann ausschließlich mit einer Zustimmung im Einzelfall (ZiE) oder vorhabenbezogener Bauartgenehmigung (vBg) möglich. Beides ist mit erheblichem Mehraufwand und höheren Kosten verbunden, was für den Lehmmauerwerksbau einen entscheidenden Nachteil im Vergleich zum konventionellen Mauerwerksbau darstellt. Um eine unkomplizierte Praxisanwendung von Lehmmauerwerk zu ermöglichen, werden innerhalb eines Forschungsvorhabens, welches von der Bundesanstalt für Materialforschung und -prüfung (BAM), dem Institut für Massivbau der Technischen Universität Darmstadt sowie dem Ingenieurbüro ZRS durchgeführt wird, auf Basis der Produktnormen für Lehmsteine und Lehmmauermörtel Grundlagen für ein aktualisiertes Bemessungskonzept entwickelt. Im Rahmen des Projekts wird ebenfalls untersucht, inwiefern die Bemessungsregeln nach den vereinfachten Berechnungsmethoden für unbewehrtes Mauerwerk gemäß DIN EN 1996-3 / NA auf den Lehmmauerwerksbau übertragbar sind. Falls eine Anwendung des normativen Nachweisverfahrens bei Lehmmauerwerk möglich ist, wäre zukünftig eine Aufnahme von Lehmsteinen und Lehmmauermörtel in den nationalen Anhang des Eurocode 6 denkbar, was eine bedeutsame Erweiterung des Anwendungsbereichs von Lehmmauerwerk zur Folge hätte.
Diverse wissenschaftliche Untersuchungen konnten zeigen, dass bemessungsrelevante Festigkeits- und Verformungseigenschaften von Lehmmauerwerksbaustoffen stark von der vorherrschenden Materialfeuchte abhängen, welche im hygroskopischen Wassergehaltsbereich im Wesentlichen von der relativen Luftfeuchte (RLF) bestimmt wird. Auch die Zwischenergebnisse des laufenden Forschungsvorhabens zur Entwicklung eines Bemessungskonzepts für Lehmmauerwerk ergaben eine deutlich erkennbare Abhängigkeit zwischen der Materialfeuchte und den relevanten Festigkeits- und Verformungseigenschaften. Eine detaillierte Kenntnis des feuchteabhängigen Materialverhaltens von Lehmsteinen, -mörtel und -mauerwerk ist zwingend erforderlich, um ein konsistentes und zuverlässiges Bemessungskonzept für Lehmmauerwerk zu entwickeln. Im Rahmen des laufenden Forschungsprojektes werden deshalb umfangreiche Versuche zum Drucktragverhalten von Lehmsteinen und -mörtel sowie Lehmmauerwerk nach Konditionierung bei unterschiedlichen RLF durchgeführt und analysiert. Weiterhin werden auf Basis der experimentellen Ergebnisse numerische Modelle kalibriert, welche die detaillierte Analyse der Biegedrucktragfähigkeit von Lehmmauerwerk ermöglichen.
In diesem Beitrag werden zunächst die bisherigen Zwischenergebnisse bezüglich der experimentell ermittelten feuchteabhängigen Materialkennwerte von Lehmsteinen, -mörtel und -mauerwerk dargelegt. Darauf aufbauend werden numerische Untersuchungen zur Bestimmung der Systemtragfähigkeit von Lehmmauerwerk unter Feuchteeinflusses durchgeführt und erläutert.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils.
Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
Influence of moisture and grain sizes on the analysis of nutrients in agricultural soils using LIBS
(2018)
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression(PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.