Emad Elwakil

A model for predicting failure of oil pipelines

Oil and gas pipelines transport millions of dollars of goods everyday worldwide. Even though they are the safest way to transport petroleum products, pipelines do still fail generating hazardous consequences and irreparable environmental damages. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models were limited to one failure type, such as corrosion failure, or relied mainly on expert opinion analysis. The objective of this paper is to develop a model that predicts the failure cause of oil pipelines based on factors other than corrosion. Two models are developed to help decision makers predict failure occurrence. Regression analysis and artificial neural networks (ANNs) models were developed based on historical data of pipeline accidents. The two models were able to satisfactory predict pipeline failures due to mechanical, operational, corrosion, third party and natural hazards with an average validity of 90% for the regression model and 92% for the ANN model. The developed models assist decision makers and pipeline operators to predict the expected failure cause(s) and to take the necessary actions to avoid them.

INVESTIGATION AND MODELING OF CRITICAL SUCCESS FACTORS IN CONSTRUCTION ORGANIZATIONS

Profit and success are considered the main drivers of any organization. Achieving this success is based on many factors which have a direct effect on the performance of these organizations. Predicting construction organizations performance helps define the weak organization points in order to improve its performance and increase the profit. In construction organizations, it is more difficult to achieve or maintain a scientific strategy to measure their current success due to the diversity and complexity of construction organizations. Previous studies used questionnaires and interviews with technical and professional persons. However, most of these studies concentrated on the critical success factors on project level. The scope of this study is to investigate the most significant organizational success factors with focus on construction organizations. This paper aims at determining most significant (i.e. critical) success factors, and to develop a model to predict the company performance based on these critical success factors. The potential success factors were surveyed from the literature study. A questionnaire was prepared for evaluating the effect of those potential success factors on organizational performance. The data collected were analyzed using Artificial Neural Networks (ANNs). Neuro-Shell software was used to rank the potential success factors utilizing the data obtained from different construction organizations. The critical success factors were used in-turn to develop a NN prediction performance model of construction organizations. The model can be used to predict the performance of a construction organization based on estimated values of its success factors.

A Framework for Performance Assessment of Organizations in the Construction Industry

Competition and customer needs forced construction companies (organizations) to measure their performance beyond the financial aspects. Success, the main driver of any organization, depends mainly on a variety of factors that impact organizations performance. Predicting the performance of a construction organization helps in identifying the weak points and in searching solutions to improve its performance and increase profit. Due to the diversity and complexity of construction organizations, it is intricate to adopt a scientific strategy that measures their success. Previous research works showed a lack of attention towards modeling organizations non-financial performance while focusing on measures of project success. Therefore, the objective of the present research is to identify and study the success factors and to develop performance prediction model(s) for construction organizations. The potential success factors are collected from literature and practitioners through a questionnaire that is prepared and sent to evaluate the eect of these potential success factors on organizational performance. The collected data are analyzed using artificial neural network (ANN) to determine the most significant (critical) success factors. Two performance prediction models are developed using ANN and regression analysis, which show robust results when verified and tested. The analysis shows that the developed models are sensitive to the identified critical success factors.

Data Management for Construction Processes Using Fuzzy Approach

Uncertainty is an entrenched characteristic of most construction projects. Most research works in simulating construction operations have focused predominantly on modeling and has neglected to study the effect of subjective variables on simulation process. Data mining is used to extract hidden knowledge from a data set, which would not be readily obtained by traditional methods. There is a significant need for a new generation of techniques and tools with the ability to automatically assist humans in analyzing the mountains of available construction data searching for useful knowledge. The presented research develops, using Fuzzy approach, a data mining engine to utilize, analyze, extract and model the hidden patterns of the project data sets to predict the work task durations. The engine depends on finding the relation between quantitative and qualitative variables, which affect the construction processes, and work task durations. It consists of five steps: (1) select the factors that affect the construction process; (2) build Fuzzy sets; (3) generate Fuzzy rules and models; (4) build Fuzzy knowledge base; and (5) validate the effectiveness of the built knowledge base to predict the work task durations. The developed engine is validated and verified using case study with sound and satisfactory results, 92 % average validity percent. The developed research/engine benefits both researchers and practitioners because it provides robust knowledge base for construction processes and a tool to predict the related task durations for construction activities.

Integrating analytical hierarchy process and regression for assessing construction organizations’ performance

Abstract This study proposes a new organization performance assessment model to assist in identifying areas with potential improvements, thus leading to better performance. The model is based on the integration of analytical hierarchy process (AHP) and multiple linear regression (MLR) analysis. The goal of this research is to design a comprehensive performance assessment model through identifying and ranking a set of critical success factors (CSFs). The research findings indicate that the CSFs in construction organizations have different priorities and weights according to different functional units. Four assessment models are developed to reflect the different perspectives of four functional units in construction organizations. The validation results of the models range from 88 to 85%. The models will benefit organizations in assessing their performance according to the different individuals’ perspectives.

Construction productivity model using fuzzy approach

Productivity is one of the most important elements to manage construction projects especially with regards to the prediction of the activities’ durations. Uncertainty is an entrenched characteristic of most construction projects. Most research works in simulating construction productivity have focused predominantly on modeling and have neglected to study the effect of subjective variables on productivity of construction process. The unique nature of construction projects and uncertainty of the construction processes lead to a need of new generation of models that utilizes the historical data. The presented research develops, using Fuzzy approach, a model to utilize, analyze, extract and find the hidden patterns of the project data sets to predict the construction process productivity. The engine depends on finding the relation between quantitative and qualitative variables, which affect the construction processes, and productivity. The methodology of this research consists of six steps: (1) Investigate the factors affecting the productivity (2) select the critical factors that affect the productivity; (3) build Fuzzy sets; (4) generate Fuzzy rules and models; (5) build Fuzzy knowledge base; and (6) validate the effectiveness of the built model to predict the construction process productivity. The developed model is validated and verified using case study with sound and satisfactory results, 90.65 % average validity percent. The developed research/engine benefits both researchers and practitioners because it provides robust model for construction processes and a tool to predict the productivity of construction processes.

Fuzzy-Based Model For Electric Lighting Evaluation In Institutional Buildings

Lighting energy consumption is the major source of energy consumption in the United States. As a result, various behavioral models that have arisen from field studies may provide the predicting personal action of artificial lighting. This paper introduces a new methodology for analyzing and predicting artificial lighting switching patterns in workplaces within institutional buildings. The methodology is based on a hierarchical fuzzy expert system approach that begins by evaluating the various factors’ performance through a set of three factors that are environmental, physical, and users’ attitudes. The fuzzy expert system is utilized to determine the weight for each factor and to aggregate all of the previous factors into one single crisp output. Finally, fuzzy logic technique is applied, which allows the aggregation of all previous indicators into one lighting performance scale that depicts the personal action of the lighting switching patterns. This study investigates the occupants’ preference factors of daylighting intensity in the workplace as occupants’ presence and behavior in buildings as well as environmental and physical factors have a large impact on electric lighting usage. The data is collected using a questionnaire from occupants of different institutional buildings. In all, the developed research/model will help architects and practitioners design efficient workplace daylighting and reduce artificial lighting energy use.

Integrating AHP-Fuzzy Model for Assessing Construction Organizations’ Performance

Organizations performance assessment is a critical aspect in today’s project management research. Construction organizations face difficulties in performance assessment, stemming from the uncertain, fragmented, and unique nature of construction industry. Most of the research neglected the different perspectives of construction organizations’ functional units when assessing their performance. Therefore, the goal of this research is to design a comprehensive performance assessment model through identifying and ranking a set of critical success factors (CSFs). Four assessment models are developed to reflect the different perspectives of four functional units in construction organizations. Analytical Hierarchy Process and Fuzzy Expert System are used for data analysis and models development. The research findings indicate that the CSFs factors in construction organizations have different priorities and weights according to different functional units. The validation results range from 84% to 93%. Overall, performance assessment models will benefit organizations in assessing performance according to the perspectives of different individuals.

Functional Units based Model for Construction Organizations Performance

Organizational performance is one of the most critical aspects in project management research. Predicting performance allows organizations to identify areas with highest improvement potentials, thus leading to higher performance and profit. The objective of this research is to identify and analyze the critical success factors to develop a comprehensive performance assessment model(s) based on values perceived from different functional units within the organization. The research hypothesis is that the perception of critical success factors will be different from one unit to another, which was proved from the results. The data was analyzed using multiple linear regression analysis to identify the significant success factors to each functional unit. The developed models reflect different perspective of four functional units in each organization. The models are validated with satisfactory average validity percent between 95.5% and 98%. The models will benefit organizations to predict an accurate performance based on the different organizations’ individuals perspective.

Framework to identify and evaluate the cause of conflicts within a Matrix organization in construction industry using fuzzy expert system

Construction industry is expanding by leaps and bounds, so are the sizes of organizations. Experts have focused on understanding and identifying a suitable organization structure that matches the needs of the construction industry. The need for a functional organization structure that is more flexible and can tackle the challenges of competitive industry led to adoption of Matrix Management. Considered as one of the ideal structures for effective control of decentralized units, it allows organizations to be more flexible and adaptive to trends. However, Matrix organizations have also been associated with attributes of ambiguity, confusion and inefficiency due to conflicts between various functional roles. The objective of this paper is to understand and identify the reasons of conflicts between various functional units within an organization. The proposed model attempts to identify the sources of conflict in early stages. It is a preliminary study that aims to develop a tool that aids top management in identifying potential conflict areas and supports in making critical decisions.