Construction of a 5D duration and cost optimisation model based on genetic algorithm and BIM

He, Wei; Shi, Yichao; Kong, Dewei

doi:10.1108/jedt-12-2018-0214

Cited by 16 publications

(9 citation statements)

References 26 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GAS demonstrated a 6.68% cost reduction to the actual schedules on average. The optimisation results showed more reduction in cost than the BIM-based time-cost optimisation proposed by (He et al 2019) which reduces by 0.62% in cost. The BIM-based automatic planning method developed by (Wang and Rezazadeh Azar 2019) showed 13.27% reductions in time compared to planned activities, although costs are not evaluated.…”

Section: Discussionmentioning

confidence: 82%

Graph-Based Automated Construction Scheduling without the Use of BIM

Hong

Xie

Agapaki

et al. 2023

J. Constr. Eng. Manage.

View full text Add to dashboard Cite

The construction industry has been suffering from delay and cost overrun for decades. Experienced schedulers programs and allocate contingencies (both cost and time) based on professional experiences and gained knowledge. Such tacit knowledge has not been captured, stored, and shared with inexperienced schedulers. This paper proposes a Graph-based Automated Scheduling (GAS) method to capture, store and reuse the tacit knowledge in the construction schedules. The proposed GAS method takes construction schedules as input, extracts schedule features, classifies schedules into different types of sequences, selects and assembles sequences into schedules, and eventually optimises time-and cost-efficiency of assembled schedules. The GAS method is validated on two case studies. The results indicate that the automatically generated construction schedule is, on average, 6.70% closer to the actual schedule than the planned schedule . Different from existing automatic scheduling methods, GAS relies little on the availability and data richness of BIM models. Hence, GAS helps schedulers initiate new schedules more efficiently at the early construction stages

show abstract

Section: Discussionmentioning

confidence: 82%

Graph-Based Automated Construction Scheduling without the Use of BIM

Hong

Xie

Agapaki

et al. 2023

J. Constr. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…Asiedu and Gyadu-Asiedu (2019) used a Bayesian Markov Chain Monte Carlo simulation model and a regression analysis model to predict construction duration overrun. He et al (2019) used 5D duration and cost optimization to develop a model based on genetic algorithm and building information modeling. The study established that this method was reasonable and effective in dealing with the complicated problem of period and cost as GA accelerated the optimization process and yielded a reliable Pareto solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling duration of building renovation projects

Lawal¹,

Ahmadu

Abdullahi

et al. 2023

JFMPC

View full text Add to dashboard Cite

Purpose This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors. Design/methodology/approach The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data. Findings Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models. Research limitations/implications The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects. Practical implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it. Social implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage. Originality/value The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.

show abstract

“…In order to create a five-dimensional construction time-cost optimization model with the benefits of optimization and simulation, He, et al [55] integrated the BIM process with GA. Rahmani Asl, et al [56] proposed an integrated framework for BIM-based performance optimization to minimize the energy consumption while maximizing the efficient daylighting level for a residential dwellings. Sekhar and Maheswari [57] aimed to study the impact of BIM on managing and reducing change orders in off-site construction by optimizing design via visualization throughout the planning phase.…”

Section: Applications Of Building Information Modellingmentioning

confidence: 99%

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

Shishehgarkhaneh¹,

Azizi²,

Basiri³

et al. 2022

Preprint

View full text Add to dashboard Cite

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

show abstract

Construction of a 5D duration and cost optimisation model based on genetic algorithm and BIM

Cited by 16 publications

References 26 publications

Graph-Based Automated Construction Scheduling without the Use of BIM

Graph-Based Automated Construction Scheduling without the Use of BIM

Modeling duration of building renovation projects

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

Contact Info

Product

Resources

About