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Erscheinungsjahr
- 2020 (33) (entfernen)
In Germany, bridges have an average age of 40 years. A bridge consumes between 0.4% and 2% of its construction cost per year over its entire life cycle. This means that up to 80% of the construction cost are additionally needed for operation, inspection, maintenance, and destruction. Current practices rely either on paperbased inspections or on abstract specialist software. Every application in the inspection and maintenance sector uses its own data model for structures, inspections, defects, and maintenance. Due to this, data and properties have to be transferred manually, otherwise a converter is necessary for every data exchange between two applications. To overcome this issue, an adequate model standard for inspections, damage, and maintenance is necessary. Modern 3D models may serve as a single source of truth, which has been suggested in the Building Information Modeling (BIM) concept. Further, these models offer a clear visualization of the built infrastructure, and improve not only the planning and construction phases, but also the operation phase of construction projects. BIM is established mostly in the Architecture, Engineering, and Construction (AEC) sector to plan and construct new buildings. Currently, BIM does not cover the whole life cycle of a building, especially not inspection and maintenance. Creating damage models needs the building model first, because a defect is dependent on the building component, its properties and material. Hence, a building information model is necessary to obtain meaningful conclusions from damage information. This paper analyzes the requirements, which arise from practice, and the research that has been done in modeling damage and related information for bridges. With a look at damage categories and use cases related to inspection and maintenance, scientific literature is discussed and synthesized. Finally, research gaps and needs are identified and discussed.
In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.
Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
The amount of adsorbed styrene acrylate copolymer (SA) particles on cementitious surfaces at the early stage of hydration was quantitatively determined using three different methodological approaches: the depletion method, the visible spectrophotometry (VIS) and the thermo-gravimetry coupled with mass spectrometry (TG–MS). Considering the advantages and disadvantages of each method, including the respectively required sample preparation, the results for four polymer-modified cement pastes, varying in polymer content and cement fineness, were evaluated.
To some extent, significant discrepancies in the adsorption degrees were observed. There is a tendency that significantly lower amounts of adsorbed polymers were identified using TG-MS compared to values determined with the depletion method. Spectrophotometrically generated values were lying in between these extremes. This tendency was found for three of the four cement pastes examined and is originated in sample preparation and methodical limitations.
The main influencing factor is the falsification of the polymer concentration in the liquid phase during centrifugation. Interactions in the interface between sediment and supernatant are the cause. The newly developed method, using TG–MS for the quantification of SA particles, proved to be suitable for dealing with these revealed issues. Here, instead of the fluid phase, the sediment is examined with regard to the polymer content, on which the influence of centrifugation is considerably lower.
Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.
Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey.
Die Mahlung als Zerkleinerungsprozess stellt seit den Anfängen der Menschheit eine der wichtigsten Verarbeitungsformen von Materialien aller Art dar - von der Getreidemahlung, über das Aufschließen von Heilkräutern in Mörsern bis hin zur Herstellung von Tonern für Drucker und Kopierer. Besonders die Zementmahlung ist in modernen Gesellschaften sowohl ein wirtschaftlicher als auch ein ökologischer Faktor. Mehr als zwei Drittel der elektrischen Energie der Zementproduktion werden für Rohmehl- und Klinker- bzw. Kompositmaterialmahlung verbraucht. Dies ist nur ein Grund, warum der Mahlprozess zunehmend in den Fokus vieler Forschungs- und Entwicklungsvorhaben rückt. Die Komplexität der Zementmahlung steigt im zunehmenden Maße an. Die simple „Mahlung auf Zementfeinheit“ ist seit langem obsolet. Zemente werden maßgeschneidert, mit verschiedensten Kombinationsprodukten, getrennt oder gemeinsam, in unterschiedlichen Mahlaggregaten oder mit ganz neuen Ansätzen gefertigt. Darüber hinaus gewinnt auch der Sektor des Baustoffrecyclings, mit allen damit verbundenen Herausforderungen, immer mehr an Bedeutung. Bei der Fragestellung, wie der Mahlprozess einerseits leistungsfähige Produkte erzeugen kann und andererseits die zunehmenden Anforderungen an Nachhaltigkeit erfüllt, steht das Mahlaggregat im Mittelpunkt der Betrachtungen. Dementsprechend gliedert sich, neben einer eingehenden Literaturrecherche zum Wissensstand, die vorliegende Arbeit in zwei übergeordnete Teile:
Im ersten Teil werden Untersuchungen an konventionellen Mahlaggregaten mit in der Zementindustrie verwendeten Kernprodukten wie Portlandzementklinker, Kalkstein, Flugasche und Hüttensand angestellt. Um eine möglichst effektive Mahlung von Zement und Kompositmaterialien zu gewährleisten, ist es wichtig, die Auswirkung von Mühlenparametern zu kennen. Hierfür wurde eine umfangreiche Versuchsmatrix aufgestellt und
abgearbeitet. Das Spektrum der Analysemethoden war ebenfalls umfangreich und wurde sowohl auf die gemahlenen Materialien als auch auf die daraus hergestellten Zemente und Betone angewendet. Es konnte gezeigt werden, dass vor allem die Unterscheidung zwischen Mahlkörpermühlen und mahlkörperlosen Mühlen entscheidenden Einfluss auf die Granulometrie und somit auch auf die Zementperformance hat. Besonders stark wurden die Verarbeitungseigenschaften, insbesondere der Wasseranspruch und damit auch das Porengefüge und schließlich Druckfestigkeiten sowie Dauerhaftigkeitseigenschaften der aus diesen Zementen hergestellten Betone, beeinflusst. Bei Untersuchungen zur gemeinsamen Mahlung von Kalkstein und Klinker führten ungünstige Anreicherungseffekte des gut mahlbaren Kalksteins sowie tonigen Nebenbestandteilen zu einer schlechteren Performance in allen Zementprüfungen.
Der zweite Teil widmet sich der Hochenergiemahlung. Die dahinterstehende Technik wird seit Jahrzehnten in anderen Wirtschaftsbranchen, wie der Pharmazie, Biologie oder auch Lebensmittelindustrie angewendet und ist seit einiger Zeit auch in der Zementforschung anzutreffen. Beispielhaft seien hier die Planeten- und Rührwerkskugelmühle als Vertreter genannt. Neben grundlegenden Untersuchungen an Zementklinker
und konventionellen Kompositmaterialien wie Hüttensand und Kalkstein wurde auch die Haupt-Zementklinkerphase Alit untersucht. Die Hochenergiemahlung von konventionellen Kompositmaterialien generierte zusätzliche Reaktivität bei gleicher Granulometrie gegenüber der herkömmlichen Mahlung. Dies wurde vor allem bei per se reaktivem Zementklinker als auch bei latent-hydraulischem Hüttensand beobachtet. Gemahlene Flugaschen konnten nur im geringen Maße weiter aktiviert werden. Der generelle Einfluss von Oberflächenvergrößerung, Strukturdefekten und Relaxationseffekten eines Mahlproduktes wurden eingehend untersucht und gewichtet. Die Ergebnisse bei der Hochenergiemahlung von Alit zeigten, dass die durch Mahlung eingebrachten Strukturdefekte eine Erhöhung der Reaktivität zur Folge haben. Hierbei konnte festgestellt werden, das maßgeblich Oberflächendefekte, strukturelle (Volumen-)defekte und als Konterpart Selbstheilungseffekte die reaktivitätsbestimmenden Faktoren sind. Weiterhin wurden Versuche zur Mahlung von Altbetonbrechsand durchgeführt. Im Speziellen wurde untersucht, inwieweit eine Rückführung von Altbetonbrechsand, als unverwertbarer Teil des Betonbruchs, in Form eines Zement-Kompositmaterials in den Baustoffkreislauf möglich ist. Die hierfür verwendete Mahltechnik umfasst sowohl konventionelle Mühlen als auch Hochenergiemühlen. Es wurden Kompositzemente mit variiertem Recyclingmaterialanteil hergestellt und auf grundlegende Eigenschaften untersucht. Zur Bewertung der Produktqualität wurde der sogenannte „Aktivierungskoeffizient“ eingeführt. Es stellte sich heraus, dass die Rückführung von Altbetonbrechsand als potentielles Kompositmaterial wesentlich vom Anteil des Zementsteins abhängt. So konnte beispielsweise reiner Zementstein als aufgemahlenes Kompositmaterial eine bessere Performance gegenüber dem mit Gesteinskörnung beaufschlagtem Altbetonbrechsand ausweisen. Bezogen auf die gemessenen Hydratationswärmen und Druckfestigkeiten nahm der Aktivierungskoeffzient mit fallendem Abstraktionsgrad ab. Ebenfalls sank der Aktivierungskoeffizient mit steigendem Substitutionsgrad. Als Vergleich wurden dieselben Materialien in konventionellen Mühlen aufbereitet. Die hier erzielten Ergebnisse können teilweise der Hochenergiemahlung als gleichwertig beurteilt werden. Folglich ist bei der Aktivierung von Recyclingmaterialien weniger die Mahltechnik als der Anteil an aktivierbarem Zementstein ausschlaggebend.
A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
(2020)
Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.