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Demolition is a high-risk operation in construction projects that may lead to serious accidents. Risk assessment is a fundamental step in managing demolition risk and preventing casualties and financial losses. The present study aims to provide a framework to identify, analyse and evaluate the risks in building demolition operations.

According to previous studies and the use of expert knowledge, 10 possible risks of the building demolition operation were identified. Subsequently, these risks were assessed using a combination of fuzzy logic with fault tree analysis (FTA), criteria importance through inter-criteria correlation (CRITIC) and technique for order preference by similarity to ideal solution (TOPSIS). Then, the risks were classified with the help of a risk decision matrix (RDM), and appropriate treatment strategies were presented according to the level of importance of each risk.

Considering the obtained magnitude for each risk and its rating, building collapse and noise pollution were identified as the most and least significant risks, respectively. The results of this study were in good agreement with the data provided by the Iranian Ministry of Cooperatives, Labour and Social Welfare, as well as obtained results of the previous studies on demolition.

This paper provides a novel framework for assessing the risks in building demolition operations. The findings of this study help demolition project managers to manage the risks in their projects properly.

This article investigates recent studies of construction law in different areas, including civil engineering; construction building technology; transportation; multidisciplinary studies and the environment, as well as their changing trends in the years between 2000 and 2019.

The transformation trend of construction law is investigated based on collecting main keywords from the Web of Science (WoS) database selectively from different viewpoints and using Scientometric Analysis by CiteSpace and HistCite software. The top journals, top universities, and the most active countries in publishing and expanding construction law, keyword co-occurrence network, top keywords with the strongest citation bursts, cluster analysis, the most cited articles are determined both generally and yearly.

By interpreting the Scientometric results, focal points of legal issues and their changing trends during the last two decades are reviewed. Scholars’ data concerning interesting topics, construction law industry future needs, knowledge gaps, and speculation about future views and direction are obtained.

Restrictions on data search, limiting the category of studies to a specific domain, and limiting research time to 20 years are some limitations of this article.

All these results address legal issues, comprehensive laws, plenary contracts, and efficient dispute resolution based on cultures and themes.

Given the importance of legal issues at all stages of the construction cycle, a review of this multidisciplinary and new science over the past two decades can provide hot issues, knowledge gaps, and a better view of the future.

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.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

US sanctions have been a major feature of US Iran policy since Iran’s 1979 Islamic revolution, but the imposition of UN and worldwide bilateral sanctions on Iran that began in 2006 and increased dramatically as of 2010 is recent by comparison. The objectives of US sanctions have evolved over time. Broad international sanctions imposed on Iran harmed Iran’s economy and contributed to Iran’s acceptance of agreements that exchange constraints on its nuclear program for sanctions relief. The subject of this study is important because both Iran and the international communities are demanding for information about the effect of sanctions on Iran. In an international and regional perspective, it seems that sanctions have a negative impact on economic, social and even political status of Iran. Therefore, this paper aims to examine the impact of Iran Central Bank sanction on Tehran Stock Exchange as on December 31, 2011.

Variables of model are consisted by exchange rate, oil prices and Tehran Stock Exchange Price Index (TEPIX) from October 2, 2011 to March 29, 2012, which is offered daily. To analyze the model, the authors used Johansen–Juselius and Autoregressive Distributed Lag (ARDL) methods.

The results indicate that there is a long-run equilibrium relationship between selected variables as oil prices, and exchange rates have a positive effect on the TEPIX. In other words, the results of the econometric estimation show the positive effect of the Iran Central Bank sanction on the TEPIX. Thus, because of economic sanctions imposed by the Western countries, Tehran Stock Exchange has been growing.

No empirical research exists that examines the impact of sanctions on stock price in developing countries. This study fills this gap by examining the links between sanctions and stock price in Iran.