969 research outputs found

    Preliminary Study on Runway Pavement Friction Decay Using Data Mining

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    AbstractSurfaces of airport pavements are subject to the friction decay phenomenon. A recurrent problem for the runways is represented by the deposits of vulcanized rubber of aircraft tires. This happens mainly in the touch-down areas during landing operations, and the loss of grip compromises the safety of both take-off and landing operations. This study moves from the International Civil Aviation Organization and the Italian Civil Aviation Authority provisions concerning runway friction measurement and reporting to a better way to analyze friction data. Being data mining the computational process of discovering patterns in a large data sets, data mining techniques are very helpful to reach this target. Unsupervised and supervised classification methods to analyze friction data detected by Grip Tester Trailer were employed. First, K-means and Subtractive Clustering were applied to divide data into a certain number of clusters representing the different areas of consumption. In a second time two different Classification and Regression Trees models, CART and GCHAID, were employed to split the data points of the runway into nodes. At the end of the process scatterplots were built and better visualized through non-linear regressions. The decay curves obtained were of service to compare the results achieved using data mining techniques versus the International Civil Aviation Organization and the Italian Civil Aviation Authority provisions in order to find out the best way to analyze friction data. The final goals are to assure an optimum scheduling of the Airport Pavement Management System, as well as users safety

    QUANTIFYING THE EFFECT OF CONSTRUCTION SITE FACTORS ON CONCRETE QUALITY, COSTS AND PRODUCTION RATES

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    Factors affecting concrete can be categorized as structured factors or unstructured factors. The first group of factors consists of those related to the production process of concrete including water-cement ratio, properties of raw materials and mix proportions. Unstructured factors or construction site factors are related to labor skills and local conditions during the construction process of a project. Concrete compressive strength as a quality metric, costs and production rates may be affected significantly by such factors while performing concrete operations at the jobsite. Several prior studies have investigated the effect of structured factors on concrete. However literature is limited regarding the effects of unstructured factors during the construction phase of a facility. This study proposes a systematic methodology to identify and quantify the effects of construction site factors including crew experience, compaction method, mixing time, curing humidity and curing temperature on concrete quality, costs and production rates using fuzzy inference systems. First, the perceived importance of construction-related factors is identified and evaluated through literature review and a survey deployed to construction experts. Then, the theory of design of experiments (DOE) is used to conduct a full 25 factorial experiment consisting of 32 runs and 192 compressive strength tests to identify statistically significant unstructured factors. Fuzzy inference systems (FISs) are proposed for predicting concrete compressive strength, costs and production rate effects through the use of adapted network-based fuzzy inference system (ANFIS). Finally, an optimization model is formulated and tested for managing concrete during the construction process of a facility. Literature review and survey results showed that curing humidity, crew experience, and compaction method are the top three factors perceived by construction experts to affect concrete compressive strength, whereas crew experience, mixing time and compaction method are the top three factors affecting concrete costs and production rates. Additionally, crew experience, compaction and mixing time were found to dominate global ranking of perceived affecting factors through the application of the relative importance index (RII). When conducting designed experiments and analysis of variance (ANOVA), compaction method, mixing time, curing humidity and curing temperature were identified to be statistically significant construction site factors for concrete compressive strength whereas crew experience, compaction method and mixing time were statistically significant factors for cost and production rates. A Sugeno type fuzzy inference system (FIS) for quantifying compressive strength, cost and production rate effects was created by using ANFIS, having correlation coefficients (R-squared values) greater than 93%, indicating that resulting models predict new observations well. Curing temperature (i.e., on-site curing temperature) was identified to be the most affecting condition for concrete compressive strength while mixing time had the biggest impact on concrete cost and production rates. The developed FISs can be used as a decision–support tool that allows for determining desired operating conditions, that ensures specified compressive strength, saves resources and maximizes profits when fabricating, placing and curing concrete

    Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

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    Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM)clusteringwere also used to developmodels for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although themajority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the othermodels. The ANFISmodels have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season

    Analogical classification in formal grammar

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    The organization of the lexicon, and especially the relations between groups of lexemes is a strongly debated topic in linguistics. Some authors have insisted on the lack of any structure of the lexicon. In this vein, Di Sciullo & Williams (1987: 3) claim that “[t]he lexicon is like a prison – it contains only the lawless, and the only thing that its inmates have in commonis lawlessness”. In the alternative view, the lexicon is assumed to have a rich structure that captures all regularities and partial regularities that exist between lexical entries.Two very different schools of linguistics have insisted on the organization of the lexicon. On the one hand, for theories like HPSG (Pollard & Sag 1994), but also some versions of construction grammar (Fillmore & Kay 1995), the lexicon is assumed to have a very rich structure which captures common grammatical properties between its members. In this approach, a type hierarchy organizes the lexicon according to common properties between items. For example, Koenig (1999: 4, among others), working from an HPSG perspective, claims that the lexicon “provides a unified model for partial regularties, medium-size generalizations, and truly productive processes”. On the other hand, from the perspective of usage-based linguistics, several authors have drawn attention to the fact that lexemes which share morphological or syntactic properties, tend to be organized in clusters of surface (phonological or semantic) similarity (Bybee & Slobin 1982; Skousen 1989; Eddington 1996). This approach, often called analogical, has developed highly accurate computational and non-computational models that can predict the classes to which lexemes belong. Like the organization of lexemes in type hierarchies, analogical relations between items help speakers to make sense of intricate systems, and reduce apparent complexity (Köpcke & Zubin 1984). Despite this core commonality, and despite the fact that most linguists seem to agree that analogy plays an important role in language, there has been remarkably little work on bringing together these two approaches. Formal grammar traditions have been very successful in capturing grammatical behaviour, but, in the process, have downplayed the role analogy plays in linguistics (Anderson 2015). In this work, I aim to change this state of affairs. First, by providing an explicit formalization of how analogy interacts with grammar, and second, by showing that analogical effects and relations closely mirror the structures in the lexicon. I will show that both formal grammar approaches, and usage-based analogical models, capture mutually compatible relations in the lexicon

    Analogical classification in formal grammar

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    The organization of the lexicon, and especially the relations between groups of lexemes is a strongly debated topic in linguistics. Some authors have insisted on the lack of any structure of the lexicon. In this vein, Di Sciullo & Williams (1987: 3) claim that “[t]he lexicon is like a prison – it contains only the lawless, and the only thing that its inmates have in commonis lawlessness”. In the alternative view, the lexicon is assumed to have a rich structure that captures all regularities and partial regularities that exist between lexical entries.Two very different schools of linguistics have insisted on the organization of the lexicon. On the one hand, for theories like HPSG (Pollard & Sag 1994), but also some versions of construction grammar (Fillmore & Kay 1995), the lexicon is assumed to have a very rich structure which captures common grammatical properties between its members. In this approach, a type hierarchy organizes the lexicon according to common properties between items. For example, Koenig (1999: 4, among others), working from an HPSG perspective, claims that the lexicon “provides a unified model for partial regularties, medium-size generalizations, and truly productive processes”. On the other hand, from the perspective of usage-based linguistics, several authors have drawn attention to the fact that lexemes which share morphological or syntactic properties, tend to be organized in clusters of surface (phonological or semantic) similarity (Bybee & Slobin 1982; Skousen 1989; Eddington 1996). This approach, often called analogical, has developed highly accurate computational and non-computational models that can predict the classes to which lexemes belong. Like the organization of lexemes in type hierarchies, analogical relations between items help speakers to make sense of intricate systems, and reduce apparent complexity (Köpcke & Zubin 1984). Despite this core commonality, and despite the fact that most linguists seem to agree that analogy plays an important role in language, there has been remarkably little work on bringing together these two approaches. Formal grammar traditions have been very successful in capturing grammatical behaviour, but, in the process, have downplayed the role analogy plays in linguistics (Anderson 2015). In this work, I aim to change this state of affairs. First, by providing an explicit formalization of how analogy interacts with grammar, and second, by showing that analogical effects and relations closely mirror the structures in the lexicon. I will show that both formal grammar approaches, and usage-based analogical models, capture mutually compatible relations in the lexicon

    Analogical classification in formal grammar

    Get PDF
    The organization of the lexicon, and especially the relations between groups of lexemes is a strongly debated topic in linguistics. Some authors have insisted on the lack of any structure of the lexicon. In this vein, Di Sciullo & Williams (1987: 3) claim that “[t]he lexicon is like a prison – it contains only the lawless, and the only thing that its inmates have in commonis lawlessness”. In the alternative view, the lexicon is assumed to have a rich structure that captures all regularities and partial regularities that exist between lexical entries.Two very different schools of linguistics have insisted on the organization of the lexicon. On the one hand, for theories like HPSG (Pollard & Sag 1994), but also some versions of construction grammar (Fillmore & Kay 1995), the lexicon is assumed to have a very rich structure which captures common grammatical properties between its members. In this approach, a type hierarchy organizes the lexicon according to common properties between items. For example, Koenig (1999: 4, among others), working from an HPSG perspective, claims that the lexicon “provides a unified model for partial regularties, medium-size generalizations, and truly productive processes”. On the other hand, from the perspective of usage-based linguistics, several authors have drawn attention to the fact that lexemes which share morphological or syntactic properties, tend to be organized in clusters of surface (phonological or semantic) similarity (Bybee & Slobin 1982; Skousen 1989; Eddington 1996). This approach, often called analogical, has developed highly accurate computational and non-computational models that can predict the classes to which lexemes belong. Like the organization of lexemes in type hierarchies, analogical relations between items help speakers to make sense of intricate systems, and reduce apparent complexity (Köpcke & Zubin 1984). Despite this core commonality, and despite the fact that most linguists seem to agree that analogy plays an important role in language, there has been remarkably little work on bringing together these two approaches. Formal grammar traditions have been very successful in capturing grammatical behaviour, but, in the process, have downplayed the role analogy plays in linguistics (Anderson 2015). In this work, I aim to change this state of affairs. First, by providing an explicit formalization of how analogy interacts with grammar, and second, by showing that analogical effects and relations closely mirror the structures in the lexicon. I will show that both formal grammar approaches, and usage-based analogical models, capture mutually compatible relations in the lexicon

    Analogical classification in formal grammar

    Get PDF
    The organization of the lexicon, and especially the relations between groups of lexemes is a strongly debated topic in linguistics. Some authors have insisted on the lack of any structure of the lexicon. In this vein, Di Sciullo & Williams (1987: 3) claim that “[t]he lexicon is like a prison – it contains only the lawless, and the only thing that its inmates have in commonis lawlessness”. In the alternative view, the lexicon is assumed to have a rich structure that captures all regularities and partial regularities that exist between lexical entries.Two very different schools of linguistics have insisted on the organization of the lexicon. On the one hand, for theories like HPSG (Pollard & Sag 1994), but also some versions of construction grammar (Fillmore & Kay 1995), the lexicon is assumed to have a very rich structure which captures common grammatical properties between its members. In this approach, a type hierarchy organizes the lexicon according to common properties between items. For example, Koenig (1999: 4, among others), working from an HPSG perspective, claims that the lexicon “provides a unified model for partial regularties, medium-size generalizations, and truly productive processes”. On the other hand, from the perspective of usage-based linguistics, several authors have drawn attention to the fact that lexemes which share morphological or syntactic properties, tend to be organized in clusters of surface (phonological or semantic) similarity (Bybee & Slobin 1982; Skousen 1989; Eddington 1996). This approach, often called analogical, has developed highly accurate computational and non-computational models that can predict the classes to which lexemes belong. Like the organization of lexemes in type hierarchies, analogical relations between items help speakers to make sense of intricate systems, and reduce apparent complexity (Köpcke & Zubin 1984). Despite this core commonality, and despite the fact that most linguists seem to agree that analogy plays an important role in language, there has been remarkably little work on bringing together these two approaches. Formal grammar traditions have been very successful in capturing grammatical behaviour, but, in the process, have downplayed the role analogy plays in linguistics (Anderson 2015). In this work, I aim to change this state of affairs. First, by providing an explicit formalization of how analogy interacts with grammar, and second, by showing that analogical effects and relations closely mirror the structures in the lexicon. I will show that both formal grammar approaches, and usage-based analogical models, capture mutually compatible relations in the lexicon

    Analogical classification in formal grammar

    Get PDF
    The organization of the lexicon, and especially the relations between groups of lexemes is a strongly debated topic in linguistics. Some authors have insisted on the lack of any structure of the lexicon. In this vein, Di Sciullo & Williams (1987: 3) claim that “[t]he lexicon is like a prison – it contains only the lawless, and the only thing that its inmates have in commonis lawlessness”. In the alternative view, the lexicon is assumed to have a rich structure that captures all regularities and partial regularities that exist between lexical entries.Two very different schools of linguistics have insisted on the organization of the lexicon. On the one hand, for theories like HPSG (Pollard & Sag 1994), but also some versions of construction grammar (Fillmore & Kay 1995), the lexicon is assumed to have a very rich structure which captures common grammatical properties between its members. In this approach, a type hierarchy organizes the lexicon according to common properties between items. For example, Koenig (1999: 4, among others), working from an HPSG perspective, claims that the lexicon “provides a unified model for partial regularties, medium-size generalizations, and truly productive processes”. On the other hand, from the perspective of usage-based linguistics, several authors have drawn attention to the fact that lexemes which share morphological or syntactic properties, tend to be organized in clusters of surface (phonological or semantic) similarity (Bybee & Slobin 1982; Skousen 1989; Eddington 1996). This approach, often called analogical, has developed highly accurate computational and non-computational models that can predict the classes to which lexemes belong. Like the organization of lexemes in type hierarchies, analogical relations between items help speakers to make sense of intricate systems, and reduce apparent complexity (Köpcke & Zubin 1984). Despite this core commonality, and despite the fact that most linguists seem to agree that analogy plays an important role in language, there has been remarkably little work on bringing together these two approaches. Formal grammar traditions have been very successful in capturing grammatical behaviour, but, in the process, have downplayed the role analogy plays in linguistics (Anderson 2015). In this work, I aim to change this state of affairs. First, by providing an explicit formalization of how analogy interacts with grammar, and second, by showing that analogical effects and relations closely mirror the structures in the lexicon. I will show that both formal grammar approaches, and usage-based analogical models, capture mutually compatible relations in the lexicon
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