A review of leading composite indicators: making a case for their use in Caribbean economies

In this article, three issues relating to leading composite indicators (LCI) are discussed: their importance, methods of estimation and uses by institutions worldwide. This discussion is utilised to provide lessons that could be learnt for the application of these indicators to the countries of the Caribbean. The principal message of this material is that in this geographical area, LCI would be important tools for economic decision makers to employ to forecast the future state of the economy. This option should be pursued vigorously by putting the necessary resources into developing the high frequency real sector data that is required for a successful application of the LCI methodology.Business cycles; Leading indicators

Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality

An expert system to optimize cost and schedule of heavy earthmoving operations for earth- and rock- filled dam projects

Success of major embankment dam construction projects is measured by the enormity of optimizing costs and schedules of selected heavy equipment based on their operational analyses. In this paper, the main objective is geared towards developing a knowledge-based decision support system for optimizing costs of heavy earthmoving operations and corresponding linear schedules at early design stages. Also, the proposed system is capable of generating an auto­mated linear schedule based on stochastic scheduling techniques. Thus, a meta-heuristic simulated approach utilizing a metropolis algorithm is implemented to assist in generating optimized line-of balances. The successful implementation of the proposed system will provide the user with optimum fleet of equipment for performing earthwork operations and linear scheduling for strategic planning purposes. Towards the end, an actual dam construction project is utilized to numerically validate the proposed system and quantify its degree of accuracy. Results presented in this study are an­ticipated to be of major significance to owners, designers, and construction managers specialized in embankment dams heavy earthmoving operations and would contribute to the database of fleet management systems by incorporating a novel system that integrates heavy equipment economical operational analyses with its corresponding line of balance. First published online: 15 Dec 201

Fuzzy approach to construction activity estimation

Past experience has shown that variations in production rate value for the same work item is attributed to a wide range of factors. The relationships between these factors and the production rates are often very complex. It is impossible to describe an exact mathematical causal relationship between the qualitative factors(QF) and production rates. Various subjective approaches have been attempted to quantify the uncertainties contained in these causal relationships. This thesis presents one such approach by adopting a fuzzy set theory in conjunction with a fuzzy rule based system that could improve the quantification of the qualitative factors in estimating construction activity durations and costs. A method to generate a Standard Activity Unit Rate(SAUR) is presented. A construction activity can be defined by combining the Design Breakdown Structure, Trade Breakdown Structure and Work Section Breakdown Structure. By establishing the data structure of an activity, it is possible to synthesis the SAUR from published estimating sources in a systematic way. After the SAUR is defined, it is then used as a standard value from which an appropriate Activity Unit Rate(AUR) can be determined. A proto-type fuzzy rule based system called 'Fuzzy Activity Unit Rate Analyser(FAURA)' was developed to formalise a systematic framework for the QF quantification process in determining the most likely activity duration/cost. The compatibility measurement method proposed by Nafarieh and Keller has been applied as an inference strategy for FAURA. A computer program was developed to implement FAURA using Turbo Prolog. FAURA was tested and analysed by using a hypothetical bricklayer's activity in conjunction with five major QF as the input variables. The results produced by FAURA iii show that it can be applied usefully to overcome many of the problems encountered in the QF quantification process. In addition, the analysis shows that a fuzzy rule base approach provides the means to model and study the variability of AUR. Although the domain problem of this research was in estimation of activity duration/cost, the principles and system presented in this study are not limited to this specific area, and can be applied to a wide range of other disciplines involving uncertainty quantification problems. Further, this research highlights how the existing subjective methods in activity duration/cost estimation can be enhanced by utilising fuzzy set theory and fuzzy logic

An expert system applied to earthmoving operations and equipment selection

The thesis represents an effort to assess the current and future development of expert systems relating to civil engineering problems. It describes the development and evaluation of an Expert System (ESEMPS) that is capable of advising on earth allocation and plant selection for road construction similar to that of an expert in the domain. [Continues.

BIM for infrastructure: An overall review and constructor perspective

The subject of building information modelling (BIM) has become a central topic to the improvement of the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the concept is being expanded into domains it was not originally conceived to address. Transitioning BIMinto the domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its use from the constructor perspective to review and analyse the current industry positioning and research state of the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. Fromthe analysis, the related research gapswere identified regarding information integration, alignment of BIM processes to constructor business processes & the effective governance and value of information. From this a unique research strategy utilising a framework for information governance coupled with a graph based distributed data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems