• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.53-62
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• 2015

Time-Cost Trade-Off (TCTO) model is an important model in the construction project planning and control area. Two types of Existing TCTO model, continuous and discrete TCTO model, have been developed by researchers. However, Using only one type of model has a limitation to represent a realistic crash scenario of activities in the project. Thus, this paper presents a comprehensive TCTO model that combines a continuous and discrete model. Additional advanced features for non-linear relationship, incentive, and liquidated damage are included in the TCTO model. These features make the proposed model more applicable to the construction project. One CPM network with 6 activities is used to explain the proposed model. The model found an optimal schedule for the example to satisfy all the constraints. The results show that new model can represent more flexible crash scenario in TCTO model.

• Journal of the Korea Institute of Building Construction
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• v.8 no.5
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• pp.67-73
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• 2008

Recent trends in construction towards larger scale and taller buildings are causing problems by ineffective existing management approach in construction industry have emerged. Delivering necessary materials and mobilizing the human resources and equipment In a timely manner to keep labor on schedule have become a critical issue to be addressed. In particular, many specialty contractors carrying out multiple projects have been experiencing difficulties mobilizing the manpower on time and in right places due to poor communication at each stage of labor supply, resulting in waste of valuable resources. Hence, it's imperative for the specialty contractors to obtain specific information on labor demand so as to set up a communication and labor management system to ensure the right human resources will be mobilized in the right place at the right time. The study therefore is aimed at developing an optimal human resources management system for specialty contractors performing multiple projects. To that end, the study is focused on rebar and concrete work. The outcome of the study is expected to help allocate the right human resources to the right place in a timely fashion, thereby achieving an effective workflow at construction sites.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.89-97
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• 2016

This paper addresses the problem of the construction planning of artillery positions, for which we present an optimization model and propose a heuristic algorithm to solve problems of practical size. The artillery position construction plan includes the assignment of engineers to support the artillery and the schedule of the support team construction sequence. Currently, in the army, managers construct the plan based on their experience. We formulate the problem as a mixed integer program and present a heuristic that utilizes the decomposition of the mixed integer model. We tested the efficacy of the proposed algorithm by conducting computational experiments on both small-size test problems and large-size practical problems. The average optimality gap in the small-size test problem was 6.44% in our experiments. Also, the average computation time to solve the large-size practical problems consisting of more than 200 artillery positions was 79.8 seconds on a personal computer. The result of our computational experiments shows that the proposed approach is a viable option to consider for practical use.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.144-152
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• 2004

Defining value as a measure of how well the project value objectives are met, Value Management Process (VMP) is considered to be any management effort or process that can proactively pursue one or more project objectives (i.e., security/safety, cost effectiveness, schedule optimization, and risk containment). The collection of 44 VMPs has been established based on a rigorous effort conducted by Construction Industry Institute (CII). Because varying circumstances on each project determine the level of suitability, it is crucial to identify which VMP should be implemented on a particular project. The current VMP selection process is primarily based on human intuition. The main objective of this paper is to provide a systematic method to facilitate the usage of VMPs on a particular project. This paper identified and quantified the selection principles (i.e., targeted value objectives, timing of initiation, project characteristics, and relative impact). The data collected from industry practitioners and VMP experts characterized each VMP in terms of the magnitude of benefit. An automated selection tool by Visual Basic Application (VBA) on MS Excel TM, was developed and proved its validity. As a pioneering study, this paper provides a comprehensive and structured knowledge on the subject of VMPs. From the industry's perspective, the automated selection tool, the premier of this study, contributes the facilitation of the VMP implementations in the construction industry thereby maximizing the potential benefits to a particular project.

• Journal of the Korea Institute of Building Construction
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• v.14 no.5
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• pp.467-476
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• 2014

According to the expanding of construction machinery works, the number of civil complaints demanding compensations are increasing continuously from surrounding residents due to the noise from construction fields. However, the noise is usually managed restrictedly during the construction phase rather than prevented in advance. So, the efforts to solve the noise problem are occurring only after complaints have been made. Also, excessive cost and time consuming in order to solve the complaints negatively affects to construction companies. Therefore, the purpose of this study is to develop an optimized scheduling process model for controlling the noise in construction field by considering the planned time, cost, and the number of equipment before construction. In addition, this process model is expected to provide a useful information about the cost comparison between the original planned cost plus compensation and the optimized cost considering noise limitation so that the site managers can manage their projects effectively.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.33-43
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• 2007

The pickup and delivery problem with time windows generally involves the construction of optimal routeswhich satisfy a set of transportation requests under pairing, precedence, time window, vehicle capacity, andavailability constraints. In this paper, we added some constraints to the problem and adopted an objectivefunction based on number of used vehicles, total travel distance and total schedule duration to consider morerealistic problems. A branch and price algohthm for the problem is proposed and an enumeration method is usedfor the subproblems. The algorithm was tested on randomly generated instances and computational results werereported.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.147-159
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• 1991

Stochastic convexity(concvity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through the probabilistic construction based on the sample path approach. A Markov additive process is obtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or for optimal operation schedule of a wide range of stochastic systems. We also clarify the conditions for stochatic monotonicity of the Markov process, which is required for stochatic convexity of the Markov additive process. This result shows that stochastic convexity can be used for the analysis of probabilistic models based on birth and death processes, which have very wide application area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.76-88
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• 1991

Stochastic convexity (concavity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through probabilistic construction based on the sample path approach. A Markov additive process is abtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or optimal operation schedule wide range of stochastic systems. We also clarify the conditions for stochastic monotonicity of the Markov process. From the result it is shown that stachstic convexity can be used for the analysis of probabilitic models based on birth and death processes, which have very wide applications area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.

• Ocean Systems Engineering
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• v.6 no.2
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• pp.143-159
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• 2016

Cargo, such as a Tension Leg Platform (TLP), Semi-submersible platform (Semi), Spar or a circular Floating Production Storage and Offloading (FPSO), are frequently dry-transported on a Heavy Lift Vessel (HLV) from the point of construction to the point of installation. The voyage can span months and the overhanging portions of the hull can be subject to frequent wave slamming events in rough weather. Tie-downs or sea-fastening are usually provided to ensure the safety of the cargo during the voyage and to keep the extreme responses of the cargo, primarily for the installed equipment and facilities, within the design limits. The proper design of the tie-down is dependent on the accurate prediction of the wave slamming loads the cargo will experience during the voyage. This is a difficult task and model testing is a widely accepted and adopted method to obtain reliable sea-fastening loads and extreme accelerations. However, it is crucial to realize the difference in the inherent stiffness of the instrument that is used to measure the tri-axial sea fastening loads and the prototype design of the tie-downs. It is practically not possible to scale the tri-axial load measuring instrument stiffness to reflect the real tie-down stiffness during tests. A correlation method is required to systematically and consistently account for the stiffness differences and correct the measured results. Direct application of the measured load tends to be conservative and lead to over-design that can reflect on the overall cost and schedule of the project. The objective here is to employ the established correlation method to provide proper high-frequency responses to topsides and hull design teams. In addition, guidance for optimizing tie-down design to avoid damage to the installed equipment, facilities and structural members can be provided.