Identification of latent legal knowledge in differing site condition (DSC) litigations

Mahfouz, Tarek; Kandil, Amr; Davlyatov, Sukhrob

doi:10.1016/j.autcon.2018.06.011

Cited by 23 publications

(6 citation statements)

References 12 publications

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“… Publications examining project performance and success estimation (10%) are generally targeted toward project management, the majority focusing on decision support for the project manager, or the discipline and process of project management itself (Hajdasz, 2015;Gudauskas et al, 2015;Hanna et al, 2018;Mahfouz et al, 2018;Vickranth et al, 2019). Other studies focus on predicting and optimizing project performance, time, and cost (Mirahadi and Zayed, 2016;Jaber et al, 2019) or project evaluation (Erzaij et al, 2020).…”

Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

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The use of artificial intelligence (AI) in construction projects has surged in recent years and is believed to represent a significant potential for increasing productivity and efficiency in the industry. The purpose of this paper is to present a state-of-the-art view of the field by conducting a review of publications concerning the topic of AI in construction and comparing the findings to previously conducted reviews. This paper provides an overview of the recent and current uses of AI in construction projects, through a descriptive analysis of the characteristics and contents of 86 peer-reviewed articles from 2015 to 2020. Although the application of AI in the industry is not entirely new, construction appears to currently be behind other industries in terms of adopting and adapting to AI. The results show that a wide range of research is conducted on AI in construction projects. A limited number of publication channels and authors stand behind a significant part of the reviewed publications. Most studies are conceptual or use a mixed-methods research design. The research addresses several areas of application, but there is a predominance of quantitatively based subfields of construction, such as estimation and cost control, logistics, planning, and scheduling. Future research should focus on developing holistic and process-oriented frameworks for projects to move from ambition to practice. Findings can inform the future development and implementation of AI in the construction industry context. For researchers, this study identifies areas in need of further attention and examines possibilities for future exploration of multidisciplinary approaches that combine construction engineering, project management, AI, and social science. For practitioners, the study highlights current trends and work within the field, providing an overview of the potential for pilot studies, tests, and innovations.

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Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

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“…Less applications are found for all the other methods. Machine learning techniques are useful for making estimations and predictions based on past data and project cases [76,105,112]. These techniques have the ability the ability to handle large amounts of datasets [34].…”

Section: Discussionmentioning

confidence: 99%

Decision Support in Building Construction: A Systematic Review of Methods and Application Areas

2020

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Decision making is a relevant task in the building construction sector, and various systems and methods for decision support are emerging. By means of a systematic literature review, this article identifies the methods for decision making in building construction and the lifecycle phases for which decision support systems are proposed. The selected articles are analyzed and grouped according to the adopted decision-making methods and the defined lifecycle phases. The findings show that multiple criteria decision analysis is the most used method for decision support in building construction and that the construction phase is the most addressed phase within the relevant existing works. The findings related to the construction phase are further refined by grouping the articles into application areas and by reviewing in detail the proposed methods therein. The scarce availability of data and project cases is identified as the most common barrier for the successful development and implementation of decision support systems in the building construction sector. This work provides a basis for scientists and practitioners for identifying suitable methods for decision-making support in a specific lifecycle phase of a building.

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“…Using the same dataset of litigated cases filed in Illinois courts, several ML based models were developed to predict the outcomes of court rulings including ANN [25], Boosted Decision Trees (BDT) [26], and two hybrid systems [27,28] that achieved 66.67%, 89.59%, 91.15%, and 96.02% accuracy, respectively. Specific to disputes caused by differences in site conditions, Mahfouz et al [29] reviewed the links between 15 legal factors and litigation outcomes using several ML techniques, which led to the highest accuracy of 88.00% from the Naïve Bayes (NB) model. Although the advantages of predicting the outcomes prior to litigation are evident such that a party can keep away from courts upon identification of an unfavorable result, the mentioned studies do not offer any alternatives to litigation.…”

Section: Research Backgroundmentioning

confidence: 99%

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

2022

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This paper compares classification performances of machine learning (ML) techniques for forecasting dispute resolutions in construction projects, thereby mitigating the impacts of potential disputes. Findings revealed that resolution cost and duration, contractor type, dispute source, and occurrence of changes were the most influential factors on dispute resolution method (DRM) preferences. The promising accuracy of the majority voting classifier (89.44%) indicates that the proposed model can provide decision-support in identification of potential resolutions. Decision-makers can avoid unsatisfactory processes using these forecasts. This paper demonstrated the effectiveness of ML techniques in classification of DRMs, and the proposed prediction model outperformed previous studies.

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Identification of latent legal knowledge in differing site condition (DSC) litigations

Cited by 23 publications

References 12 publications

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

Decision Support in Building Construction: A Systematic Review of Methods and Application Areas

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

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