Incorporating aleatory and epistemic uncertainties in the modelling of construction duration

Ahmadu, Hassan Adaviriku; Ibrahim, A. M.; Ibrahim, Yahaya Makarfi; Adogbo, Kulomri Jipato

doi:10.1108/ecam-06-2019-0304

Cited by 8 publications

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Construction project duration and cost are also related, the classical duration-cost model is T= KC B , T is duration; C is cost; K and B are coefficients to be determined (Bromilow et al, 1980), the coefficients to be determined vary according to the project type (Kaka and Price, 1991), and the later improved duration-cost model, which introduces the variables of total floor area and number of total floors ( Chan and Chan, 2004). In addition, the choice of forecasting tools, the effect of cognitive uncertainty, contractor management capability, frequent design changes and initial over-optimism on the part of the owner can also affect construction duration (Ahmadu et al, 2020;Velumani et al, 2021;Doloi et al, 2012). Understanding the interrelationships and the degree of influence of the factors affecting the duration can help decision makers and managers of construction projects to effectively master the key factors with sufficient resources (Peng et al, 2022).…”

Section: Selection Of Indicatorsmentioning

confidence: 99%

Research on residential building duration prediction model based on mean clustering and neural network

Ji,

Nan,

Wei

et al. 2023

Preprint

View full text Add to dashboard Cite

The duration target will directly affect the effectiveness of the implementation of the residential building project, so it is necessary to predict a reasonable residential building duration. In this work, genetic algorithm (GA) is used to optimize and improve the weights and thresholds of the BP neural network to form a GA-BP neural network model, and seven parameters, such as aboveground building structure, number of building floors, building area, decoration standards, foundation structure, number of underground building floors, and underground building area, are taken as the input parameters of the neural network model, and the residential building duration is taken as the output parameter. 111 sets of residential building duration data were collected and divided into 90 training sets and 21 test sets, and the model was validated and analyzed in comparison using root mean square error (RMSE), correlation coefficient (R) and average error rate, which were used to validate that the GA-BP neural network model helps in predicting the duration of residential buildings. In order to improve the prediction accuracy of GA-BP neural network model, this work uses artificial bee colony improved K-means clustering algorithm to classify 111 sets of experimental data and 33 sets of new data, according to the classification of the training and testing results show that the ABC-K-means-GA-B model all have a strong generalization ability and strong prediction accuracy, and at the same time, it proves that this paper's proposed ABC- K-means-GA-BP neural network helps to predict the construction period of residential buildings, which is of great practical significance for improving construction efficiency.

show abstract

Section: Selection Of Indicatorsmentioning

confidence: 99%

Research on residential building duration prediction model based on mean clustering and neural network

Ji,

Nan,

Wei

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Research works have also been conducted to develop multivariate construction time predicting models combining project scope (quantitative factors) and other non-project scope factors (qualitative factors) (Chan and Chan, 2002;Hoffman et al, 2007;Ahmadu et al, 2015). More recently, efforts have been made to incorporate the impact of uncertainty in predicting the duration of construction projects (Ahmadu et al, 2020).…”

mentioning

confidence: 99%

Modeling duration of building renovation projects

Lawal¹,

Ahmadu

Abdullahi

et al. 2023

JFMPC

Self Cite

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

Exploring the Potentials of Artificial Intelligence in Managing Project Deliverables and Risk in Construction Projects: A Bibliometric Analysis

Kelemu,

Rotimi,

Samarasinghe

2024

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Incorporating aleatory and epistemic uncertainties in the modelling of construction duration

Cited by 8 publications

References 62 publications

Research on residential building duration prediction model based on mean clustering and neural network

Research on residential building duration prediction model based on mean clustering and neural network

Modeling duration of building renovation projects

Exploring the Potentials of Artificial Intelligence in Managing Project Deliverables and Risk in Construction Projects: A Bibliometric Analysis

Contact Info

Product

Resources

About