Quantification of the inherent uncertainty in the relaxation modulus and creep compliance of asphalt mixes

Advanced material characterization of asphalt concrete is essential for realistic and accurate performance prediction of flexible pavements. However, such characterization requires rigorous testing regimes that involve mechanical testing of a large number of laboratory samples at various conditions and set-ups. Advanced measurement instrumentation in addition to meticulous and accurate data analysis and analytical representation are also of high importance. Such steps as well as the heterogeneous nature of asphalt concrete (AC) constitute major factors of inherent variability. Thus, it is imperative to model and quantify the variability of the needed asphalt material’s properties, mainly the linear viscoelastic response functions such as: relaxation modulus, \(E(t)\), and creep compliance, \(D(t)\). The objective of this paper is to characterize the inherent uncertainty of both \(E(t)\) and \(D(t)\) over the time domain of their master curves. This is achieved through a probabilistic framework using Monte Carlo simulations and First Order approximations, utilizing \(E^{*}\) data for six AC mixes with at least eight replicates per mix. The study shows that the inherent variability, presented by the coefficient of variation (COV), in \(E(t)\) and \(D(t)\) is low at small reduced times, and increases with the increase in reduced time. At small reduced times, the COV in \(E(t)\) and \(D(t)\) are similar in magnitude; however, differences become significant at large reduced times. Additionally, the probability distributions and COVs of \(E(t)\) and \(D(t)\) are mix dependent. Finally, a case study is considered in which the inherent uncertainty in \(D(t)\) is forward propagated to assess the effect of variability on the predicted number of cycles to fatigue failure of an asphalt mix.     

ProbExplorer: Uncertainty‐guided Exploration and Editing of Probabilistic Medical Image Segmentation

In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty‐based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time‐varying medical imaging datasets.     

Outbound logistics exception monitoring: A multi-perspective ontologies’ approach with intelligent agents

Logistics consists of a complex network of organizations and processes where exception monitoring is critical for the success of logistics service. In order to detect exceptions effectively, exception monitoring requires proper understanding of the possible exceptions. However, the extant exception monitoring approaches or systems still lack sufficient emphasis in exceptions understanding. This paper presents a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies and intelligent agents. Specially, the multi-perspective ontologies, involving static ontology, social ontology and dynamic ontology, are firstly employed to develop the taxonomy of the logistics exception, to reflect the situation dependencies of logistics exception and to represent the dynamic nature of business processes. From this point forwards, an outbound exception monitoring system is designed by introducing multi-intelligent agents, which can ensure autonomous, flexible, and collaborative exception monitor in logistics service. Finally, the presented approach and designed system are exhibited through a case study of two ubiquitous logistics exceptions, which indicates that the proposed multi-perspective ontologies provide better understanding of exceptions thereby enabling the designed outbound exception monitoring system to perform well.     

Measuring research performance of Iraqi universities using Scopus data

Scientometrics - This study investigates the research performance of the Iraqi public and private universities using the Scopus citation database. The investigation consists of three stages. The...     

A Virtual Environment for the Design and Simulated Construction of Prefabricated Buildings

The construction industry has acknowledged that its current working practices are in need of substantial improvements in quality and efficiency and has identified that computer modelling techniques and the use of prefabricated components can help reduce times, costs, and minimise defects and problems of on-site construction. This paper describes a virtual environment to support the design and construction processes of buildings from prefabricated components and the simulation of their construction sequence according to a project schedule. The design environment can import a library of 3-D models of prefabricated modules that can be used to interactively design a building. Using Microsoft Project, the construction schedule of the designed building can be altered, with this information feeding back to the construction simulation environment. Within this environment the order of construction can be visualised using virtual machines. Novel aspects of the system are that it provides a single 3-D environment where the user can construct their design with minimal user interaction through automatic constraint recognition and view the real-time simulation of the construction process within the environment. This takes this area of research a step forward from other systems that only allow the planner to view the construction at certain stages, and do not provide an animated view of the construction process.     

Dynamic evaluation of the development process of knowledge-based information systems

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system’s performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system—the cost of keeping a domain expert on-line—is reduced.     

Convergence Variability and Population Sizing in Micro-Genetic Algorithms

The issues of micro-genetic algorithm (micro-GA) convergence, population size, internal variability, and performance are examined. Procedures are developed to determine best population size when micro-GAs are used for optimization of real-world problems. Results show that the best population size cannot be determined before convergence has occurred and that convergence may occur without reaching an optimal solution. To determine the best population size, this article proposes the use of consistency of the final fitness values in addition to the fitness value at convergence. Internal variability must be considered in finding the best population size, and that best population size depends on the level of computational resources used. Smaller populations with generous computational resources performed as good as and sometimes better than larger populations. Midsized populations consistently exhibited lower variation in fitness value. For a given population size, the increase in fitness value may not be worth the added computation cost beyond a certain point. Three approaches are proposed to determine the best population size: (1) a random selection approach, (2) a detailed approach, and (3) a simplified approach. We used the detailed approach with our traffic problem and found that, with sufficient computational resources, the micro-GA performed best with population size around the square root of the string length. We tested this proposition on standard simple and deceptive problems and found it to hold true.     

Using lithium nitrate and pozzolanic glass powder in concrete as ASR suppressors

This investigation studies the influence of two different mineral admixtures, lithium nitrate (Li) and pozzolanic glass powder (PGP) on the expansion induced by alkali-silica reaction (ASR). Four numbers of concrete prisms were produced for each concrete mix to measure the expansion resulted from the ASR according to the test method of the BS 812-123:1999. Chemical analysis was performed using X-ray spectra. Test results confirmed that Li and PGP have significantly reduced the ASR expansion. Lower calcium to silica ratio (Ca/Si) was found in concrete mix contains (PGP) because of the high amorphous reactive silica and low calcium content in PGP compared to ordinary Portland cement CEM1. Similar components and minerals phases were obtained in different concrete mixes by using XRD.     

A discrete-time internal model-based controller and its application to a binary distillation column

Explicit state-space formulae for an ^∞ two degrees-of-freedom (2-DOF) Internal Model Control (IMC)-based controller are derived. The IMC controller solves a special ^∞ control problem directly in discrete-time, providing robust stability against coprime factor uncertainty, and a degree of robust performance in the sense of making the closed-loop system match a pre-specified reference model. The IMC controller structure is shown to comprise a plant state observer, an on-line step response model, and a generalised state-feedback law associated with the plant and model states. A design for a binary (methanol/water) distillation column is presented as an illustrative example.