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Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This study aims to develop a comprehensive conceptual framework that serves as a foundation for identifying most critical delay risk drivers for Building Information Modelling (BIM)-based construction projects.

A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify key delay risk drivers in BIM-based construction projects that have significant impact on the performance of delay risk predictive modelling techniques.

The results show that contractor related driver and external related driver are the most important delay driver categories to be considered when developing delay risk predictive models for BIM-based construction projects.

This study contributes to the body of knowledge by filling the gap in lack of a conceptual framework for selecting key delay risk drivers for BIM-based construction projects, which has hampered scientific progress toward development of extremely effective delay risk predictive models for BIM-based construction projects. Furthermore, this study's analyses further confirmed a positive effect of BIM on construction project delay.

Almost all projects in the world are delayed, and sometimes even lead to the full bankruptcy of their beneficiaries. These delays can be calculated using techniques, but most importantly, there must be a fair and realistic division of delays between project beneficiaries. The most valid delay calculation techniques belong to the SCL Global Protocol, but they also have significant drawbacks, such as these: (1) They do not have the capability to prevent project delays (Delay Risk Management); (2) The protocol identifies and introduces any delays in activities with a ratio of one to one as a delay (Effective Delay); (3) It also does not offer the capability to share delays between stakeholders, which is a huge weakness. Floating in the base schedule activities is one of the cost control tools of projects, but it can hide project delays. In this paper, the researchers believe that the floating ownership belongs to the project and not belong to the stakeholders. This is the main tool for analyzing and sharing delays in this research.

The research methodology adopted included an extensive literature review, expert interviews, use of questionnaire and designing three innovative linked together models by researchers.

In this research, an integrated technique is introduced which has the following capabilities; delay risk control, result-based delay analysis and stakeholders delay sharing. This technique with an incursive and defensive approach implements claims management principles and calculates, respectively, non-attributable and attributable delays for each beneficiary.

This creativity led to the introduction of the Incursive and Defensive (In-De) technique; in the SCL protocol techniques, none of these capabilities exist.

Completing construction projects within the planned schedule has widely been considered as one of the major project success factors. This study investigates the use of technologies to address delays in construction projects and aims to address three research questions (1) to identify the adopted technologies and proposed solutions in the literature, (2) to explore the reasons why the delays cannot be prevented despite disruptive technologies and (3) to determine the major strategies to prevent delays in construction projects.

In total, 208 research articles that used innovative technologies, methods, or tools to avoid delays in construction projects were investigated by conducting a comprehensive literature review. An elaborative content analysis was performed to cover the implemented technologies and their transformation, highlighted research fields in relation to selected technologies, focused delay causes and corresponding delay mitigation strategies and emphasized project types with specific delay causes. According to the analysis results, a typological framework with appropriate technological means was proposed.

The findings revealed that several tools such as planning, imaging, geo-spatial data collection, machine learning and optimization have widely been adopted to address specific delay causes. It was also observed that strategies to address various delay causes throughout the life cycle of construction projects have been overlooked in the literature. The findings of the present research underpin the trends and technological advances to address significant delay causes.

Despite the technological advancements in the digitalization era of Industry 4.0, many construction projects still suffer from poor schedule performance. However, the reason of this is questionable and has not been investigated thoroughly.

The purpose of this research is to advise on UK construction delay strategies. Critical delay factors were identified and their interrelationships were explored; in addition, a predictive model was established upon the factors and interrelationships to calculate delay potentials.

The critical causes were identified by a literature review, verified by an open-ended questionnaire survey and then analysed with 299 samples returned from structured questionnaire surveys. The model consisted of factors screened out by Pearson product–moment correlational coefficient, constructed by a logical reasoning process and then quantified by conducting Bayesian belief networks parameter learning.

The technical aspect of construction project management was less critical while the managerial aspect became more emphasised. Project factors and client factors present relatively weak impact on construction delay, while contractor factors, contractual arrangement factors and distinctively interaction factors present relatively strong impact.

This research does not differentiate delay types, such as excusable vs non-excusable ones and compensable vs non-compensable ones. The model nodes have been tested to be critical to construction delay, but the model structure is mostly based on previous literature and logical deduction. Further research could be done to accommodate delay types and test the relationships.

This research updates critical delay factor list for the UK construction projects, suggesting general rules for resource allocation concerning delay avoidance. Besides, this research establishes a predictive model, assisting delay avoidance strategies on a case-by-case basis.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

The purpose of this study is to improve communication success through barrier identification and analysis so that the identified barriers can help project teams establish effective information-exchange strategies.

The recent publications on construction communication about time management are reviewed. Then, the semi-structured interviews are performed with both questionnaires and audio recordings (n1 = 18). Next, the collected data are analyzed using both statistical measures on the questionnaire survey and qualitative coding analysis on the text transcripts from an audio recording. Particularly, the identified barriers are substantiated using a scientometrics approach based on the published articles (2011–2020, n2 = 52,915) for purposeful information-sharing solutions in construction time management. Furthermore, the intervention strategies from the top 10 most-cited articles are analyzed and validated by comparisons with the results from construction surveys and relevant studies.

Based on the discussed communication difficulties, five main barriers were identified during time-cost risk management: probability and statistical concepts, availability of data from external resources, details of team member experiences, graphics (and graphical presentation skills), and spatial and temporal (a.k.a. 4D) simulation skills. For the improvement of communication skills and presentation quality regarding probability and statistical concepts, project teams should emphasize context awareness, case studies and group discussions. Details of communication techniques can be adjusted based on the backgrounds, experiences and expectations of team members.

The dataset n1 has both size and duration limits because of the availability of the invited industry professionals. The dataset n2 considers the literature from 2011 to 2020. Any before-the-date and unpublished studies are not included in the study.

A thorough comprehension of communication barriers can help project teams develop speaking, writing and analytical thinking skills that will enable the teams to better deliver ideas, thoughts and meanings. Additionally, the established discussion on barrier-removal strategies may enhance time management effectiveness, reduce project delays, avoid confusion and misunderstanding and save rework costs.

This research calls for the awareness of communication barriers in construction project execution and team collaboration. The identified barriers and the established solutions enrich the approaches of construction companies to share information with communities and society.

This is the first identification model for communication barriers in the time management of the construction industry to the authors' knowledge. The influencing factors and the countermeasures of communication difficulties highlighted by the research were not examined systematically and holistically in previous studies. The findings provide a new approach to facilitate the development of powerful communication strategies and to improve project execution.

As a developing nation, Bangladesh still has scarce technological applications in the construction sector, which results in construction delays. This paper aims to propose a framework that will diminish manual labor, reduce human error and apply four-dimensional (4D) building information modeling (BIM)-based solutions to mitigate and prevent construction project delays.

First, a systematic literature review was conducted on analyzing the construction delay scenario in the context of Bangladesh and other countries. Next, a 4D BIM-based framework was developed using Autodesk Navisworks Manage. Finally, it was used to run on-site simulations on an ongoing construction project which faced delays because of design errors and inefficient planning.

Affirmative results were found from applying these methods through real-time project simulation. The current status of the project and the status after using BIM technology were compared. It was observed that during both the preconstruction and execution phases, the application of 4D BIM could reduce the delay posed by design error and inefficient planning.

The project manager and the design engineers can use these frameworks to review their projects. For the design engineers, the preconstruction phase portion of the framework will help identify the probable errors in the design. For the project managers, keeping track of time using the execution phase portion of the framework will be resourceful.

To the best of the authors’ knowledge, this study is the first to assess the significant delay factors endemic in Bangladesh and develop a BIM-based technological solution. This study is solely dedicated to reforming the construction techniques in Bangladesh through the application of 4D BIM technology.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.