Rifat Sonmez

Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects.

INTRODUCTION The inherent and unique risks on construction projects quite often present key challenges to contractors. Health and safety risks are among the most significant risks in construction projects since the construction industry is characterized by a relatively high injury and death rate compared to other industries. In construction project management, safety risk assessment is an important step toward identifying potential hazards and evaluating the risks associated with the hazards. Adequate prioritization of safety risks during risk assessment is crucial for planning, budgeting, and management of safety related risks. METHOD In this paper, a safety risk assessment framework is presented based on the theory of cost of safety (COS) model and the analytic hierarchy process (AHP). The main contribution of the proposed framework is that it presents a robust method for prioritization of safety risks in construction projects to create a rational budget and to set realistic goals without compromising safety. THE IMPACT TO THE INDUSTRY The framework provides a decision tool for the decision makers to determine the adequate accident/injury prevention investments while considering the funding limits. The proposed safety risk framework is illustrated using a real-life construction project and the advantages and limitations of the framework are discussed.

A hybrid genetic algorithm for the discrete time-cost trade-off problem

In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete time-cost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The HA and a sole GA have been coded in Visual C++ on a personal computer. Ten benchmark test problems with a range of 18 to 630 activities are used to evaluate performance of the HA. The benchmark problems are solved to optimality using mixed integer programming technique. The results of the performance analysis indicate that the hybrid strategy improves convergence of GA significantly and HA provides a powerful alternative for the DTCTP.

Predesign cost estimation of urban railway projects with parametric modeling

Abstract This paper presents a parametric modeling method for predesign cost estimation of urban railway systems. Data of 13 light rail and metro projects located in Turkey were compiled for quantification of the impacts of parameters on the project costs. Parametric models have been developed using regression analysis and neural networks techniques. Ten linear regression models were used for determination of the parameters significantly impacting cost of urban railway projects. Two neural networks were considered as an alternative to regression models, particularly for the identification of the non‐linear relations. Predictive behaviour and performance of the models were compared to determine a model that presents adequate relations and has a reasonable accuracy. The proposed method provides a powerful approach for determination of a satisfactory parametric cost model during early project stages by incorporating a coordinated use of regression analysis and neural network techniques.

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.

Hybrid Genetic Algorithm with Simulated Annealing for Resource-Constrained Project Scheduling

AbstractResource-constrained project scheduling problem (RCPSP) is a very important optimization problem in construction project management. Despite the importance of the RCPSP in project scheduling and management, commercial project management software provides very limited capabilities for the RCPSP. In this paper, a hybrid strategy based on genetic algorithms, and simulated annealing is presented for the RCPSP. The strategy aims to integrate parallel search ability of genetic algorithms with fine tuning capabilities of the simulated annealing technique to achieve an efficient algorithm for the RCPSP. The proposed strategy was tested using benchmark test problems and best solutions of the state-of-the-art algorithms. A sole genetic algorithm, and seven heuristics of project management software were also included in the computational experiments. Computational results show that the proposed hybrid strategy improves convergence of sole genetic algorithm and provides a competitive alternative for the RCPSP...

Discrete particle swarm optimization method for the large-scale discrete time-cost trade-off problem

A novel PSO method is presented for the discrete time-cost trade-off problem (DTCTP).The proposed discrete PSO outperforms the state-of-the-art methods.High quality solutions are achieved within seconds for large-scale instances.New large scale benchmark DTCTP instances are generated and are solved to optimal. Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time-cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time-cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects.

Backward-Forward Hybrid Genetic Algorithm for Resource-Constrained Multiproject Scheduling Problem

AbstractDespite the fact that companies manage multiple projects simultaneously, most research on resource-constrained project scheduling has focused on single projects. This paper presents a backward-forward hybrid genetic algorithm (BFHGA) for optimal scheduling of a resource-constrained multiproject scheduling problem (RCMPSP). The new approach combines complementary strengths of the backward-forward scheduling method, genetic algorithms, and simulated annealing. BFHGA was tested on four single-project case examples, one portfolio case example, one real portfolio, and 26 test portfolio instances. The proposed algorithm obtained the best solution for all of the single-project case examples, and outperformed five state-of-the-art meta-heuristics and five popular heuristics for the resource-constrained multiproject scheduling problems. The computational results show that the BFHGA is a fast and effective algorithm for scheduling multiple projects with common limited resources. The performance gap between ...

Geographic information system–based visualization system for planning and monitoring of repetitive construction projects

This study presents a visualization system based on a geographic information system (GIS) for planning and monitoring the progress of construction projects that are repetitive due to their geometrical layout. A prototype system was developed and applied to an actual pipeline project to demonstrate the advantages of the proposed approach. The primary advantage of the system is improved visualization of geographical conditions and their impact on the progress rate. Enhanced visual representation of the schedule and work sequence can be achieved by time–lapse simulation. The system also provides a framework for effective communication of schedule, progress, and geographic information among the project participants. Future enhancements to the prototype system are addressed.

Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem

AbstractIntensive heuristic and metaheuristic research efforts have focused on the Pareto front optimization of discrete time-cost trade-off problem (DTCTP). However, very little success has been achieved in solving the problem for medium and large-scale projects. This paper presents a new particle swarm optimization method to achieve an advancement in the Pareto front optimization of medium and large-scale construction projects. The proposed Pareto front particle swarm optimizer (PFPSO) is based on a multiobjective optimization environment with novel particle representation, initialization, and position-updating principles that are specifically designed for simultaneous time-cost optimization of large-scale projects. PFPSO brings several benefits for the discrete time-cost optimization, such as an adequate representation of the discrete search space, fast convergence properties, and improved Pareto front optimization capabilities. The computational experiment results reveal that the new particle swarm op...

Critical Sequence Crashing Heuristic for Resource-Constrained Discrete Time–Cost Trade-Off Problem

AbstractDespite the importance of project deadlines and resource constraints in construction scheduling, very little success has been achieved in solving the resource-constrained discrete time–cost trade-off problem (RCDTCTP), especially for large-scale projects. In this paper a new heuristic method is designed and developed to achieve fast and high-quality solutions for the large-scale RCDTCTP. The proposed method is based on the novel principles to enable effective exploration of the search space through adequate selection of the activities to be crashed for a resource constrained schedule, by only crashing the activities with zero float in a resource constrained-schedule, which form the critical sequence. The computational experiment results reveal that the new critical sequence crashing heuristic outperforms the state-of-the-art methods, both in terms of the solution quality concerning project cost and computation time. Solutions with a deviation of 0.25% from the best known solutions are achieved wit...