Forecasting Construction Cost Index through Artificial Intelligence

Aslam, Bilal; Maqsoom, Ahsen; Inam, Hina; Basharat, Mubeen ul; Ullah, Fahim

doi:10.3390/soc13100219

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…The authors in [22] stated that the optimal combination of scheduled and unscheduled maintenance to ensure occupant contentment is an appropriate solution. The majority of the studies, such as the one estimating the early cost of a concrete bridge using an ANN model [23], and a study by [24] identifying the ANN as an effective method, focus on cost estimation, including predicting the Construction Cost Index (CCI). An integration model of an ANN and AHP, developed by [25], was employed to estimate the cost of road networks, and [26] utilized an ANN to predict the final cost of the construction project.…”

Section: Rehabilitation Selection Methodsmentioning

confidence: 99%

Criticality-Based Management of Facility Assets

Salman

2024

Buildings

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Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. NeuralTools 7.5 is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. Lingo 20.0 is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale.

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Section: Rehabilitation Selection Methodsmentioning

confidence: 99%

Criticality-Based Management of Facility Assets

Salman

2024

Buildings

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“…MC can foster superior reverse logistics compared to traditional construction by utilizing technological advancements like artificial intelligence, digital twins, and the Internet of Things (IoT) [105][106][107]. MC favors material traceability due to labeling its components during production, digital documentation through IoT, or material passports, which aids the reverse logistics of building components at EoL [105].…”

Section: Conceptual Framework For Mitigating Climate Change Through M...mentioning

confidence: 99%

Climate Change Mitigation through Modular Construction

Sajid,

Ullah,

Qayyum

et al. 2024

Smart Cities

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Modular construction (MC) is a promising concept with the potential to revolutionize the construction industry (CI). The sustainability aspects of MC, among its other encouraging facets, have garnered escalated interest and acclaim among the research community, especially in the context of climate change (CC) mitigation efforts. Despite numerous scholarly studies contributing to the understanding of MC, a holistic review of the prevailing literature that systematically documents the impact of utilizing MC on CC mitigation remains scarce. The study conducts a systematic literature review (SLR) of the pertinent literature retrieved from the Scopus repository to explore the relationship between MC and CC mitigation. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the SLR was conducted on 31 shortlisted articles published between 2010 and 2023. The findings of the study reveal that MC can mitigate the climate crisis by reducing GHG emissions, curtailing resource intensiveness by enabling a circular economy (CE), fomenting energy efficiency, and fostering resourceful land use and management in the CI. A conceptual framework based on the findings of the previous literature is proposed in this study, which outlines several strategies for CC mitigation that can be implemented by the adoption of MC in the CI. The current study is a humble effort to review various offerings of MC to help mitigate CC in the era of striving for global sustainability. For industry practitioners and policymakers, this study highlights the viability of leveraging MC for CC mitigation, aiming to inspire better decision making for sustainable development in the CI. Similarly, for researchers, it presents MC as a potential tool for CC mitigation that can be further explored in terms of its associated factors, and focused frameworks can be developed.

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“…Artificial Neural Networks (ANNs) are increasingly utilized in the construction industry, capitalizing on their capabilities in pattern recognition, predictive modeling, and decision-making support. Numerous researchers have employed ANNs to estimate project costs and facilitate budget allocation [15][16][17][18][19][20][21][22][23], as well as to enhance project scheduling [24][25][26][27][28]. The process of designing, training, and using Artificial Neural Networks (ANNs) involves several key stages, each contributing to the network's ability to perform tasks such as classification, regression, prediction, and more.…”

Section: The Applications Of Artificial Neural Network (Anns) In the ...mentioning

confidence: 99%

Analyzing Contingency Estimation for Residential Turnkey Projects in Saudi Arabia: A Neural Network Approach

Salman

2024

Buildings

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Utilizing a turnkey approach to deliver a construction project entails significant risks from the contractor’s perspective. Essentially, the owner awaits project completion without commitments regarding additional expenditures incurred by the contractor during the project’s duration. This paper specifically focuses on estimating and analyzing the contingency value for residential turnkey projects in Saudi Arabia. The contingency value across the project’s life cycle is estimated using six Artificial Neural Network (ANN) models, which are compared to identify the best-trained network according to project complexity, contingency factor, and contingency impact during the project phases. The output layer provides the contingency factor percentages for each project phase. A 13-story reinforced concrete (RC) residential building established in one of Saudi Arabia’s cities was selected to implement the developed methodology. The contingency estimation, performed using @Risk 7.5 and NeuralTools 7.5, was determined to be 11.34% and was distributed across the five phases of the project’s life cycle: 0.30% for predesign, 0.99% for design, 2.61% for preconstruction, 6.33% for construction, and 1.12% for postconstruction. Furthermore, it was found that the estimated contingency varies based on project complexity, which is 7.20% for low complexity, 8.16% for medium complexity, 9.41% for complicated, and 11.34% for very complicated projects. Historical data and peer review approaches are employed to validate the results, both of which are endorsed by professionals in this field. This paper highlights two main contributions: Firstly, it significantly enhances risk management by facilitating a comprehensive understanding and systematic analysis of risks, thus improving the contractors’ ability to mitigate potential negative impacts on projects. Secondly, it supports more informed decision-making through the use of advanced techniques to estimate and analyze contingency values. These contributions are critical for contractors engaged in Saudi construction projects, particularly those involving residential buildings.

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Forecasting Construction Cost Index through Artificial Intelligence

Cited by 4 publications

References 46 publications

Criticality-Based Management of Facility Assets

Criticality-Based Management of Facility Assets

Climate Change Mitigation through Modular Construction

Analyzing Contingency Estimation for Residential Turnkey Projects in Saudi Arabia: A Neural Network Approach

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