This meta-analysis has examined the past ten years’ studies concerning the causes of construction project delay. It aims to update the subject area and investigate critical causes of project delay in three different conditions of the external environment. The data from 50 studies have been analyzed and synthesized to determine the top ten critical causes of delay. The Relative Importance Index (RII) technique was applied to rank the critical causes; subsequently, the Spearman’s rank correlation coefficient was calculated to evaluate the critical causes. The review findings indicate substantial differences between the critical causes of project delay in defined situations. The top ten critical causes of delay in developed countries root in the project’s internal environment. The leading causes of delays in developing countries are from the project’s internal and task environment. While in countries with various constraints and high risk, the general environment has a critical impact alongside the project task and internal environment on time overrun of a project. Moreover, this review summarized and categorized the best available studies to propose a systematic approach in identifying critical causes of delay to bridge the existing knowledge gap.

The increasing demand for multifamily housing in the United States requires alternatives for building more affordable and sustainable housing to improve the quality of life for millions of families. Offsite construction (OSC) strategies may be a viable alternative for tackling this problem. Although the use of OSC is significant in the multifamily housing market in the world and it is also very promising in the US, a scarce amount of literature has focused on this topic. The purpose of this study is to identify specific factors that affect decisions on the use of OSC in multifamily housing in the US. Focusing on the sustainability dimensions of construction—social, environmental, and economic—the authors reviewed literature that was published between 2000 and 2019 and identified factors that are related to OSC adoption in general construction, in housing construction, and, more specifically, in multifamily housing construction in the US. Subsequently, a discussion on some important factors affecting decisions on the use of OSC in the American multifamily market is provided. The discussion focused on factors that, although important, have been under explored in the literature that addresses the use of OSC in multifamily projects, especially in the US, which are: customer’s attitude, building performance, and building comfort and indoor environmental quality (IEQ). In addition, a brief discussion regarding the importance of design to the uptake of OSC in multifamily projects is provided. This is one of the first studies dedicated to exploring the social, environmental, and economic factors that affect the use of OSC in multifamily housing in the US. The study also identifies research gaps, which serve as a roadmap for future research.

At the beginning of 2018 about 6% of California’s bridges are structurally deficient, and approximately 17% of California’s bridges are estimated to cost about $12.2 billion for repairs. The subjectivity in determining the condition rating is an imprecise process and may significantly affect the maintenance process, which may vary from inspector to another. Most research works in prestressed concrete bridges condition rating has focused predominantly on modeling and has neglected to study the effect of non-periodical maintenance on condition rating. This study aims to identify the variables affecting superstructure deterioration and build models for predicting the superstructure condition. This paper has used National Bridge Inventory for California State in order to build models for predicting the superstructure condition of four structure types (Slab; Stringer/Multi Beam or Girder; T-Beam; and Box Beam or Girder) using Regression technique and Monte Carlo simulation. This research shows the impact of eight significant variables on the superstructure deterioration with high coefficient of determination (R² = 86%). The developed models have been validated with a satisfactory result “93%” using Average Validity Percentage method. The developed models will help departments of transportation and infrastructure agencies to predict the condition rating and priorities the maintenance process for bridges..

As a natural disaster occurs, it negatively affects and destroys a large scale of urban pattern in a short period of time. Building a structure in such situations is considered a process that encompasses broad topics such as social, economic, and environmental issues linked together and interrelated. In addition, many of these tasks have conflicting nature towards one another, making it difficult to manage the process as a whole. This research aims to review: (1) The different data-mining-based methods used in post-disaster, and (2) The practice and research trends in disaster resilience and disaster risk reduction literature published in the last two decades. Finally, future work and research challenges are recommended to help the decision-making research community set a clear agenda for the upcoming research. The researchers have identified 150 academic papers from the last two decades through text mining using the ‘Engineering Village’ database. The search considered high-ranked peer-reviewed journals, conference proceedings, and most cited documents. Post-disaster housing heavily relies on tacit knowledge instead of hard measurable data like any construction field. There is a lack of decision-making studies in the post-disaster topic using hard data. Therefore, it is essential to consider any measurable factors that stem from the environment compared to relying only on expert opinion, as using solely tacit knowledge is ineffective. In addition to this, there have not been enough studies on this matter using a combination of hard data and tacit knowledge. A step towards understanding different work areas in disaster resilience, the researchers identified, categorized, and grouped the literature review based on different fields of post-disaster decision-making. Moreover, the research identifies and discusses the most frequent topics and techniques of multi-objective decision-making in post-disaster situations. This article is the first to provide a comprehensive conceptualization of the challenges and constraints for decision-making in post-disaster construction.

This study proposes a new organization performance assessment model to assist in identifying areas with potential improvements, thus leading to better performance. The model is based on the integration of analytical hierarchy process (AHP) and multiple linear regression (MLR) analysis. The goal of this research is to design a comprehensive performance assessment model through identifying and ranking a set of critical success factors (CSFs). The research findings indicate that the CSFs in construction organizations have different priorities and weights according to different functional units. Four assessment models are developed to reflect the different perspectives of four functional units in construction organizations. The validation results of the models range from 88 to 85%. The models will benefit organizations in assessing their performance according to the different individuals’ perspectives.

Asset management of healthcare facilities utilizes technical and administrative strategies to extend the asset life cycle. This research aims to develop an asset condition assessment (ACA) model for a healthcare facility to predict and assess critical asset ratings considering various factors, especially patient risk space groups, and asset redundancy. Two modelling techniques were used to develop the ACA model. First, the Analytic Hierarchy Process (AHP) was used to calculate the weighting criteria and ranking the factors. Second, the AHP and regression analysis techniques were combined into a single model to evaluate healthcare facilities' asset condition assessment. The integrated model was validated with 88.32% average validity percent (AVP). The model was determined to be robust, and its results satisfactory. Finally, this research developed a numerical asset condition assessment (ACA) critical rating scale, which can guide capital renewals leaders in predicting and managing healthcare facilities' critical infrastructure needs. Ultimately, this tool can be used to improve the asset management of healthcare facilities, which is one of the nation's largest infrastructure sectors.