Ahmed Khalafallah

Neural Network Based Model for Predicting Housing Market Performance

Abstract The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home developers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neural network based models to support real estate investors and home developers in this critical task. The paper describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to predict unforeseen future performances. An application example is analyzed to demonstrate the model capabilities in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between−2% and +2%.

Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering

In nonlinear construction optimization problems, the capability of current optimization algorithms to find an optimal solution is usually limited by their inability to evaluate the effects of changing the value of each decision variable on reaching the optimal solution. This paper presents fundamental research aimed at developing a novel evolutionary optimization algorithm, named Electimize, that mimics the behavior of electrons flowing, through electric circuit branches with the least electric resistance. In the proposed algorithm, solutions are represented by electric wires and are evaluated on two levels: a global level, using the objective function, and a local level, evaluating the potential of each generated value for every decision variable. The paper presents (1) the research philosophy and scope, (2) the research methodology, and (3) the development of the algorithm. The proposed algorithm has been validated and applied successfully to an NP-hard cash flow optimization problem. The algorithm was able to find a better optimal solution and identified ten alternative optimal solutions for the same problem. This should prove useful in enhancing the optimization of complex large-scale problems.

Using Electimize to Solve the Time-Cost-Tradeoff Problem in Construction Engineering

Construction optimization problems are difficult to solve due to the enormous number of parameters resulting from the booming in technology and the application of sophisticated systems in construction projects. In the past few decades, evolutionary algorithms (EAs) have served as good optimization techniques for solving these problems. However, many EAs are limited in their capabilities in reaching optimality due to their methods of evaluating candidate solution strings. This paper presents a newly developed evolutionary algorithm, named Electimize, with an application example on solving construction time-cost-tradeoff problem (TCTP). The new algorithm simulates the behavior of electrons moving through electric circuit branches with the least resistance. Specifically, the paper discusses: 1) the basic steps of optimization using Electimize, 2) TCTP modeling using Electimize, and 3) comparison between performances of Electimize and other EAs used to solve this problem. Electimize demonstrates an advantage over other existing evolutionary algorithms in the method used for evaluating solution strings, which is reflected by the better results obtained for the TCTP.

Framework For A Multi-Level Evolutionary Algorithm For Construction Optimization.

In large-scale non-linear construction optimization problems, the capability of an algorithm to find the optimal solution is usually limited by the inability to evaluate the effect of change in the value of each decision variable on the overall outcome of the objective function. Current optimization algorithms evaluate the quality of generated solutions based only on the value of fitness/objective function. As such, these algorithms are limited in their ability to robustly reach optimal solutions. This paper presents a framework for an innovative evolutionary algorithm that mimics the behavior of electrons moving through electric circuit branches with the least resistance. In the proposed algorithm, solutions are evaluated on two levels: a global level against the objective function; and a local level by evaluating the potential of the generated value for each decision variable. This paper presents (1) the philosophy behind this work; (2) the concept adopted in developing the algorithm; and (3) the basic steps of the algorithm. The new algorithm is expected to enhance the optimization of complex large-scale optimization problems.

Introduction of a New Optimization Mechanism for Electimize

Electimize is a new evolutionary algorithm that simulates the phenomenon of the flow of electrons and electrical conductivity. Previous research proved Electimize to be very efficient in solving NP-hard optimization problems. The algorithm demonstrates higher capabilities in searching the solution space extensively, and identifying global optimal alternatives, if any. The basic advantage of Electimize over other evolutionary algorithms (EAs) lies in the evaluation process of the quality of the solution strings. Unlike other EAs, Electimize evaluates the quality of every value in the solution string independently. Recent research showed that Electimize is slow in converging towards the optimal solution, if the size of problem is increased. This paper presents a new mechanism for enhancing the performance of Electimize by introducing new parameters that would guide the algorithm towards the optimal values. The paper discusses the methodology of the work, the basic theory behind the newly introduced parameters, and the main steps of optimization.

Handling Risks of Quantity Variations in Unit-Price Contracts

AbstractUnit-price contracts are awarded based on estimated quantities while payment to the contractor is based on actual quantities of work. In many cases, estimated and actual quantities never pe...

A Framework for Optimizing Detour Planning and Development around Construction Zones

Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners, as they are required to plan vehicle routes around construction zones in such a way that maximizes the safety of construction workers and reduce the impact on daily commuters. This paper presents a study that aims at developing a framework for optimizing the planning of construction detours. The main objectives of the study are to: 1) identify all the decision variables that affect the planning of construction detours; 2) quantify the impact of these decision variables on construction workers and daily commuters; and 3) implement a model based on shortest path formulation to identify the optimal alternatives for construction detours. The ultimate goal of this study is to offer construction planners with the essential guidelines to improve construction safety and reduce construction zone hazards, and a critical tool for selecting and optimizing construction zone detours.