Moheeb E. Ibrahim

A hybrid CAD-based construction site layout planning system using genetic algorithms

The efficient layout planning of a construction site is a fundamental task to any project undertaking. In an attempt to enhance the general practice of layout planning of construction sites, the paper introduces a novel approach for producing the sought layouts. This approach integrates the highly sophisticated graphical capabilities of computer-aided design (CAD) platforms with the robust search and optimization capabilities of genetic algorithms (GAs). In this context, GAs are utilized from within the CAD environment to optimize the location of temporary facilities on site. The functional interaction between GAs and CAD and the details of the GA-based layout optimization procedure are presented. A fully automated computer system is further developed to demonstrate the practicality of the chosen approach. In order to evaluate the systems performance, a local construction project with a 24,000m2 site is used. The automated system produced highly satisfactory results and showed notable flexibility through its CAD-based input/output media.

Sustainable construction management: introduction of the operational context space (OCS)

Sustainable construction is an emerging field of science that aims at incorporating the general sustainable development concepts into conventional construction practices. While the foundation of knowledge in this field is continuously expanding, sustainable construction is not yet standard industry practice. One major technical barrier that hinders enacting sustainable construction is the absence of an application framework that integrates both sustainability and construction practices at an operational level. This shortcoming is being addressed through a three‐dimensional operational context space (OCS) that achieves the sought integration aspect. The three dimensions of OCS are: (1) project life cycle phases; (2) project executing entities; and (3) sustainability performance parameters. Such OCS facilitates the association of responsibility, by assigning each sustainability requirement to a specific project entity (or entities) during specific project phase(s), and further provides a numerical assessment for construction projects using sustainability as a criterion. Steps of constructing the OCS and how it could be employed in the evaluation and benchmarking of a projects environmental performance are examined, along with sample illustrations in the area of construction waste management.

A simulation-based planning system for wind turbine construction

Wind turbine construction is a challenging undertaking due to the need to lift heavy loads to high locations in conditions of high and variable wind speeds. These conditions create great risks to contractors during the turbine assembly process. This paper presents a simulation-based system to aid in the construction planning of wind turbines. The system is composed of three main components; 1) A wind speed forecasting module based on artificial neural networks, 2) A series of discrete event simulation models that act as a test bed for different turbine construction methods and resource utilizations, and 3) A rule-based system that relates prevalent wind speed to the impact on lifting activity durations. Actual wind speed data from the Zafarana wind farm in Egypt is used and turbine construction productivity and resource utilization is compared for two common turbine construction methods.

A Balanced Risk Treatment For Construction Projects

Risk management is an integral part of a successful project planning and control mechanism. Standards, e.g., AS/NZS ISO 31000:2009, establish frameworks on how to perform comprehensive risk management process. However, there remain gaps in enacting such standards in reality, one of which is balancing risk treatment with the associated costs to risk-bearing project stakeholders. Although many studies were carried out to identify the range of factors representing project risk events and the recommended responses, very little has addressed the means of making such decisions. In this context, the guided search capabilities of evolutionary algorithms can play a role. After discussing and modeling the costs and benefits of alternative risk treatment strategies, the paper introduces ant colony as a capable algorithm for the balanced selection of such strategies. The research is being applied in the pipeline construction sector and made use of professional knowledge and project records from a mega construction company in the Middle East.

Valuation of Minimum Revenue Guarantees for PPP Wastewater Treatment Plants

Private sector investments in public infrastructure projects have witnessed a tremendous increase over the past decade. The lack of government resources coupled with the need for expanding new and renewing exiting infrastructure has created a viable market for private infrastructure investments. Proper allocation of risks in public private partnership (PPP) projects has been identified as one of the critical success factors of these projects. Revenue risk is a common risk item in most PPP projects due to the long contract duration of these projects. Government Minimum Revenue Guarantees (MRG) is a common risk mitigation strategy whereby the government guarantees that project revenues will not fall below a specified limit during the contract. This guarantee is only redeemable at distinct points in time, so takes the form of either a Bermudan option, or a Simple multiple-exercise real option, depending on the number of exercise rights afforded. In this paper the valuation of this real option is done through the application of the Multi-Least squares Monte, and Multi-Exercise Boundary methods. These methods combine the use of Monte Carlo simulation, and dynamic programming. The quantitative approach offers more flexibility than other prevailing methods, and facilitates the contractual and financial negotiations in such projects. Two case studies of waste water treatment plants are examined. The first considers the construction of the treatment plant in one stage while the second considers the contractual requirement of phasing the construction of the plant in two stages.

Employing Ant Colony for the Optimal Reduction of Project Risk Severity

Efforts undertaken in identifying, analyzing and assessing project risks are only made good use of when proper risk treatment strategies are decided upon and pursued. Based on the criteria established by senior management, the risk management plan goes about defining how each risk is to be handled. There are options to that end, including acceptance, avoidance, transfer and mitigation. Whilst these strategies are known to all in the industry, the decision-making process is far from easy. A research was undertaken to optimize risk treatment in construction projects, where both costs and benefits are balanced out at the project level. The paper particularly introduces Ant Colony Optimization (ACO) as a capable algorithm for the balanced selection of risk treatment strategies; that is to reduce the overall risk severity in a project at the minimum cost possible. ACO resembles the real life behavior of ants in their intelligent and guided search for food. The research is being applied in the pipeline construction sector and made use of professional knowledge and project records from a big construction company in the Middle East. The paper further presents an example project to demonstrate how ACO explores the risk treatment alternatives in a project and chooses the optimal set of strategies in such context.

Optimal Reduction of Project Risk Severity: A Case Study

Although many studies were carried out to identify the range of risky events in construction projects and the recommended response and precautionary strategies, little has addressed the means of making such decisions in an optimal way. Unfortunately, complex projects are marred with numerous interconnected causes and effects, which make project dynamics rather difficult to understand and control. A research was undertaken to optimize risk treatment in construction projects, where costs and benefits are balanced out at the project level. Study employs ant colony optimization (ACO) and dynamic risk maps (DRM) to achieve the sought research goal. This paper focuses on presenting the mechanics of the research approach via a real life case study. The construction project into consideration was executed by a well-known contractor in the Middle East. The paper first describes the case details. After the identification of project risk events, the risk inter-dependencies were modelled using a DRM, which made use of the professional knowledge of industry professionals and archived project records as well. Furthermore, ACO is utilized for the balanced selection of risk treatment strategies and to help reduce the projects overall risk severity at the minimum cost possible. Paper ends with useful insights into the research approach and outcomes of case study application.