An experimental evaluation of prediction models for the mechanical behavior of unreinforced, lime-mortar masonry under compression

This paper contributes to the understanding of lime-mortar masonry strength and deformation (which determine durability and allowable stresses/stiffness in design codes) by measuring the mechanical properties of brick bound with lime and lime-cement mortars. Based on the regression analysis of experimental results, models to estimate lime-mortar masonry compressive strength are proposed (less accurate for hydrated lime (CL90s) masonry due to the disparity between mortar and brick strengths). Also, three relationships between masonry elastic modulus and its compressive strength are proposed for cement-lime; hydraulic lime (NHL3.5 and 5); and hydrated/feebly hydraulic lime masonries respectively.

Disagreement between the experimental results and former mathematical prediction models (proposed primarily for cement masonry) is caused by a lack of provision for the significant deformation of lime masonry and the relative changes in strength and stiffness between mortar and brick over time (at 6 months and 1 year, the NHL 3.5 and 5 mortars are often stronger than the brick). Eurocode 6 provided the best predictions for the compressive strength of lime and cement-lime masonry based on the strength of their components. All models vastly overestimated the strength of CL90s masonry at 28 days however, Eurocode 6 became an accurate predictor after 6 months, when the mortar had acquired most of its final strength and stiffness.

The experimental results agreed with former stress-strain curves. It was evidenced that mortar strongly impacts masonry deformation, and that the masonry stress/strain relationship becomes increasingly non-linear as mortar strength lowers. It was also noted that, the influence of masonry stiffness on its compressive strength becomes smaller as the mortar hydraulicity increases.

An Experimental Investigation into the Effects of Flanged Holes in Hat-Stiffened Panels Loaded in Uniaxial Compression

The effects of a 100 mm diameter integrally-flanged hole in a hat-stiffenend carbon-fibre composite panel, loaded in uniaxial compression, were investigated and compared with a similar panel containing an unflanged hole. Details of the manufacturing techniques used in the production of the integral flange are presented. The stiffening effects of the flange reduced the bending strains, which may lead to high interlaminar shear strains, around the cutout while increasing the membrane strains. These membrane strains were well below the limit strains for this composite material. The skin in the unflanged hole also underwent a change in buckling mode shape from three longitudinal half-wavelengths to five half-wavelengths. No such change was observed in the flanged panel and this buckled in four longitudinal half-wavelengths. The ultimate strength of both panels was determined by the load carrying capability of the stiffeners.

Is the raised volume rapid thoracic compression technique ready for use in clinical trials in infants with cystic fibrosis?

The European Cystic Fibrosis Society Clinical Trial Network (ECFS-CTN) has established a Standardization Committee to undertake a rigorous evaluation of promising outcome measures with regard to use in multicentre clinical trials in cystic fibrosis (CF). The aim of this article is to present a review of literature on clinimetric properties of the infant raised-volume rapid thoracic compression (RVRTC) technique in the context of CF, to summarise the consensus amongst the group on feasibility and answer key questions regarding the promotion of this technique to surrogate endpoint status.

METHODS: A literature search (from 1985 onwards) identified 20 papers that met inclusion criteria of RVRTC use in infants with CF. Data were extracted and tabulated regarding repeatability, validity, correlation with other outcome measures, responsiveness and reference values. A working group discussed the tables and answered 4 key questions.

RESULTS: Overall, RVRTC in particular forced expiratory volume in 0.5s, showed good clinimetric properties despite presence of individual variability. Few studies showed a relationship between RVRTC and inflammation and infection, and to date, data remains limited regarding the responsiveness of RVRTC after an intervention. Concerns were raised regarding feasibility in multi-centre studies and availability of reference values.

CONCLUSION: The ECFS-CTN Working Group considers that RVRTC cannot be used as a primary outcome in clinical trials in infants with CF before universal standardization of this measurement is achieved and implementation of inter-institutional networking is in place. We advise its use currently in phase I/II trials and as a secondary endpoint in phase III studies. We emphasise the need for (1) more short-term variability and longitudinal 'natural history' studies, and (2) robust reference values for commercially available devices.

Semantics for possibilistic answer set programs: uncertain rules versus rules with uncertain conclusions

Although Answer Set Programming (ASP) is a powerful framework for declarative problem solving, it cannot in an intuitive way handle situations in which some rules are uncertain, or in which it is more important to satisfy some constraints than others. Possibilistic ASP (PASP) is a natural extension of ASP in which certainty weights are associated with each rule. In this paper we contrast two different views on interpreting the weights attached to rules. Under the first view, weights reflect the certainty with which we can conclude the head of a rule when its body is satisfied. Under the second view, weights reflect the certainty that a given rule restricts the considered epistemic states of an agent in a valid way, i.e. it is the certainty that the rule itself is correct. The first view gives rise to a set of weighted answer sets, whereas the second view gives rise to a weighted set of classical answer sets.

Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modelling combinatorial problems. However, ASP cannot be used easily for reasoning about uncertain information. Possibilistic ASP (PASP) is an extension of ASP that combines possibilistic logic and ASP. In PASP a weight is associated with each rule, whereas this weight is interpreted as the certainty with which the conclusion can be established when the body is known to hold. As such, it allows us to model and reason about uncertain information in an intuitive way. In this paper we present new semantics for PASP in which rules are interpreted as constraints on possibility distributions. Special models of these constraints are then identified as possibilistic answer sets. In addition, since ASP is a special case of PASP in which all the rules are entirely certain, we obtain a new characterization of ASP in terms of constraints on possibility distributions. This allows us to uncover a new form of disjunction, called weak disjunction, that has not been previously considered in the literature. In addition to introducing and motivating the semantics of weak disjunction, we also pinpoint its computational complexity. In particular, while the complexity of most reasoning tasks coincides with standard disjunctive ASP, we find that brave reasoning for programs with weak disjunctions is easier.

Using Answer Set Programming for Solving Boolean Games

Boolean games are a framework for reasoning about the rational behaviour of agents, whose goals are formalized using propositional formulas. They offer an attractive alternative to normal-form games, because they allow for a more intuitive and more compact encoding. Unfortunately, however, there is currently no general, tailor-made method available to compute the equilibria of Boolean games. In this paper, we introduce a method for finding the pure Nash equilibria based on disjunctive answer set programming. Our method is furthermore capable of finding the core elements and the Pareto optimal equilibria, and can easily be modified to support other forms of optimality, thanks to the declarative nature of disjunctive answer set programming. Experimental results clearly demonstrate the effectiveness of the proposed method.

Possibilistic Answer Set Programming Revisited

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.

Communicating Answer Set Programs

Answer set programming is a form of declarative programming that has proven very successful in succinctly formulating and solving complex problems. Although mechanisms for representing and reasoning with the combined answer set programs of multiple agents have already been proposed, the actual gain in expressivity when adding communication has not been thoroughly studied. We show that allowing simple programs to talk to each other results in the same expressivity as adding negation-as-failure. Furthermore, we show that the ability to focus on one program in a network of simple programs results in the same expressivity as adding disjunction in the head of the rules.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Answer set programs with optional rules: a possibilistic approach

Many problems in artificial intelligence can be encoded as answer set programs (ASP) in which some rules are uncertain. ASP programs with incorrect rules may have erroneous conclusions, but due to the non-monotonic nature of ASP, omitting a correct rule may also lead to errors. To derive the most certain conclusions from an uncertain ASP program, we thus need to consider all situations in which some, none, or all of the least certain rules are omitted. This corresponds to treating some rules as optional and reasoning about which conclusions remain valid regardless of the inclusion of these optional rules. While a version of possibilistic ASP (PASP) based on this view has recently been introduced, no implementation is currently available. In this paper we propose a simulation of the main reasoning tasks in PASP using (disjunctive) ASP programs, allowing us to take advantage of state-of-the-art ASP solvers. Furthermore, we identify how several interesting AI problems can be naturally seen as special cases of the considered reasoning tasks, including cautious abductive reasoning and conformant planning. As such, the proposed simulation enables us to solve instances of the latter problem types that are more general than what current solvers can handle.