Time and Cost Analysis for Highway Road Construction Project Using Artificial Neural Networks

Naik, M. Gopal; Radhika, V. Shiva Bala

doi:10.6106/jcepm.2015.5.1.026

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…Papers used a variety of machine learning algorithms in their investigation. Examples were found of support vector machine (Cheng and Hoang, 2014;Cheng et al, 2010a, b;El-Sawalhi, 2015;Kong et al, 2008;Petruseva et al, 2017;Petruvesa et al, 2013;Wauters and Vanhoucke, 2014), neural networks (Darko et al, 2023;Kim et al, 2004;Naik and Radhika, 2015;Petroutsatou et al, 2012;Wang et al, 2012;Yahia et al, 2011), random forest (Shoar et al, 2022;Yaseen et al, 2020) and regression (Kim et al, 2004(Kim et al, , 2013Mahmoodzadeh et al, 2022;Petruseva et al, 2017;Yip et al, 2014). No consistent findings were evident in what type of algorithm worked best given particular characteristics of the investigation dataset in conjunction with specific predicted characteristics, size or sectoral base.…”

Section: Data Analyticsmentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

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PurposeIn recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.Design/methodology/approachWe propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.FindingsThe literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.Originality/valueBased on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

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Section: Data Analyticsmentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

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“…Another study predicting the duration and cost of highway road projects was proposed by the authors in [ 81 ] using ANN. Data for model training were taken from past projects in which all items were uniform.…”

Section: Applications Of Artificial Intelligence In Civil Construcmentioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

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“…This model was then validated and the results indicate a better estimate of the realistic cost of highway projects in Libya. Naik and Radhika (2015) developed different ANN models for project cost and duration estimation. Hyari et al (2015) presented an ANN model for the conceptual cost estimation of engineering services for public construction projects involving both design and construction supervision costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

Meharie

Mengesha

Gariy

et al. 2021

ECAM

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PurposeThe purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.Design/methodology/approachThe proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.FindingsThe findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.Research limitations/implicationsThe study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.Originality/valueThe study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

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Time and Cost Analysis for Highway Road Construction Project Using Artificial Neural Networks

Cited by 9 publications

References 4 publications

Data analytics for project delivery: unlocking the potential of an emerging field

Data analytics for project delivery: unlocking the potential of an emerging field

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

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