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.

Modified Isolated Delay Type Technique

Construction projects are complex, from their design to the execution phase. Delivering a project on time is unpredictable due to the inherent uncertainty. Delays are normally considered to be an inseparable part of construction projects. Delays often lead to claims for costs incurred. Assessing construction claims caused by delays is complicated, as are the proceedings for achieving claim resolution. Loss of anticipated revenue, opportunity cost, increased overhead, cost escalation and liquidated damages are some of the main reasons for delay claims from key project stakeholders. A sound request for a delay claim must be supported by a reliable delay analysis technique. This paper discusses a new technique that is capable of evaluating concurrent delays. The technique is windows-based; therefore, it can trace all of the changes in the critical path(s). Apportionment of delay accountability may result in a false outcome if the effect of concurrent delays and changes in the critical path is overlooked. The procedures of this proposed technique are explained. The technique was tested against a hypothetical case and compared to existing delay analysis techniques with satisfactory results. The proposed technique allocates delays among the different project parties

INTEGRATED DECISION SUPPORT SYSTEM FOR BRIDGES AT CONCEPTUAL DESIGN STAGE

ABSTRACT Subjectivity is the main factor that affects the selection process of a bridge type at the conceptual design stage. Selecting the bridge type is generally influenced by many parameters. In most cases, these parameters are evaluated and assessed by engineers based on their own experience, knowledge and judgment. Therefore, in an attempt to provide some consistency and objectivity to the selection process, this paper proposes a methodology to develop a model that incorporates systematical procedures that can be used to avoid and limit the decision maker's subjectivity. This methodology considers "machine technique", which is a branch of Artificial Intelligence (AI), as the core for the guideline engine of the proposed model, besides using Artificial Neural Network (ANN) modeling with its back-propagation algorithm to identify and select the utmost solution. Comprehensive evaluation of bridge characteristics and parameters that influence the final decision is implemented and considered in the proposed model. The model is applicable for both general and specific cases of bridge design. Its simplicity, user friendly and comprehension would help designers minimize the influence of human subjectivity while taking major decisions; automatically rank all possible alternatives and consider all factors that impact the decision process. The process is automatic and will require minimal user intervention

Decision support method for multi-criteria selection of bridge rehabilitation strategy

Bridge management is the decision-making process for selecting and prioritizing the actions necessary to maintain a bridge within acceptable limits of safety and serviceability. The current decision-making approach for bridge management is based on optimizing the life cycle cost of the structure. This is a single criterion decision-making process which does not include the indirect impact of the maintenance, repair and replacement actions. Sound bridge management decisions should be made through balanced consideration of multiple and conflicting criteria. This requirement motivated the development of a multi-criteria decision support method for bridge deck management. The method is based on a modified analytic hierarchy process (AHP) to evaluate and rank alternative bridge rehabilitation strategies. The modified AHP provides an effective analytical tool to deal with complex decision making and has the following features: (1) multi-criteria decision-making process; (2) accounts for the uncertainty associated with the pairwise comparison values; and (3) provides a sensitive evaluation of consistency in judgements. The proposed decision support method is a rational decision-making technique for bridge management. The method practicality and validity is demonstrated using a real case study from the industry.AHP, bridge, decision, rehabilitation,

Web-based integrated project control system

This paper presents a system that supports project time and cost control in an integrated manner. The system utilizes object-oriented modelling to represent the process of project delivery. A set of control objects is designed to map the process of project control. Eighteen key indicators are considered to represent the resources utilized in each control object and serve as sensors to highlight problematic areas associated with unfavourable performance. A Three-Tier Client/Sever computer system is designed to implement the developed system. Daily, weekly, monthly and/or yearly, period-by-period, and cumulative to-date project performance reports are generated to provide the status at project, control object and resource levels. An example drawn from the literature is analysed to allow for comparison with the results obtained using the proposed methodology. The example also serves the purpose of demonstrating the use of the proposed system and illustrating its essential features.Project control, earned value, progress reporting, web-based system,

Performance Prediction of Construction Projects using Soft Computing Methods

Key Performance Indicators (KPIs) evaluate different aspects of projects and are used as a thermometer to determine the health status of projects. While there is considerable work on project quantitative performance prediction, less attention, however, has been directed towards qualitative performance. This paper offers a novel framework for qualitatively measuring and predicting six important construction project KPIs using neuro-fuzzy technique. Neuro-fuzzy models are developed to map the KPIs of three critical project stages to the whole project KPIs. Subtractive clustering is utilized to automatically generate initial Fuzzy Inference System (FIS) models and the artificial neural network (ANN) technique is used to tune the parameters of the initial FIS models. The relative weight of each KPI is determined using Analytic Hierarchy Process (AHP) and Genetic Algorithm (GA) to generate Performance Indicator (PI). This framework can be used in building construction projects to help decision-makers evaluate the performance of their projects.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity

Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man-hours required to produce the final product in comparison to planned man-hours. Productivity is a key element in determining the success and failure of any construction project. Construction as a labour-driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy. Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors. Compound irregularity is a significant issue in modeling construction labour productivity. Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. In this study, the expected productivity considering environmental and operational variables was modeled. Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity. Results show that BNN outperforms other techniques for modeling construction labour productivity