Traditional teaching methods rely solely on the use of textbooks, but teaching effectiveness assessment methods have demonstrated that most students taught by this method do not absorb the course content up to the expected level. Multiple researchers have introduced nontraditional teaching methods, but there is no scientific consensus on the best nontraditional teaching methods that are tailored to learners’ abilities while most effectively addressing the course objectives. Therefore, the goal of this review was to address the following questions across all engineering disciplines, based on learners’ abilities and the course objectives: (a) What are the benefits of nontraditional teaching methods? and (b) How would you categorize the benefits of nontraditional teaching methods? A qualitative review was conducted to achieve these goals, and the initial search for papers, using relevant keywords, resulted in more than 2000 peer-reviewed articles that were published between 2000 and 2017. A total of 125 peer-reviewed articles pertaining to the most frequently studied nontraditional teaching methods were comprehensively studied and analyzed. The analysis resulted in practical guidelines, including a list of the benefits of the five studied nontraditional teaching methods (flipped classroom, gamification, case study, self-learning, and social media) belonging to four categories: technical/professional, personal skills/ability, personal attitude, and time and space. Based on the results, the authors established significant guidelines for instructors who aim to optimize learners’ achievements by adopting the most effective teaching styles, based on their course objectives and the learners’ abilities.

An increase in the number and strength of natural catastrophes experienced over the past few decades has accelerated the damage sustained by infrastructures. Drinking water and wastewater infrastructure systems are critical aspects of a healthy environment, and their ability to withstand disasters is vital for effective disaster response and recovery. Although numerous studies have been conducted to determine the challenges that natural disasters render to water infrastructures, few extensive examinations of these challenges have been conducted. The goal of this study, therefore, was to identify and categorize the challenges related to the resilience of drinking water and wastewater infrastructures, and to determine the strategies that most effectively minimize their unintended consequences. A comprehensive evaluation of the existing literature was conducted, and 537 publications were collected. After extensive screening, 222 publications were selected for rigorous evaluation and analysis based on the data collection methods and other criteria. A total of fifty-one (51) challenges were determined and classified, within the following five categories: environmental, technical and infrastructure, social, organizational, and financial and economic. The challenges were then ranked within each category according to their frequency of occurrence in previous research. The results reveal that climate change, aging infrastructure, lack of infrastructure capital, population growth, improper maintenance of water infrastructure, and rapid urbanization are the most frequently cited challenges. Next, 30 strategies and approaches were identified and categorized into either preventive or corrective actions, according to their implementation time. The findings of this study will help decision- and policymakers properly allocate their limited funding to enhance the robustness of their water infrastructures before, during, and after natural hazards.

Many large-scale construction projects suffer from the issuance of rework that ultimately leads to substantial cost overruns and major scheduling delays. Scholars and practitioners worldwide have assessed the impact of these changes and the critical causes behind them, finding mitigation difficult after the rework has been done. Therefore, identification of the early indicators of manageable rework causes and implementation of the appropriate mitigating strategies prior to the need for rework are important. The aim of this study was to identify the indicators of manageable rework causes (IMRCs) and select the appropriate construction best practices (BPs) to reduce the cost of manageable and controllable rework in terms of the total cost of a project. To fulfill the objectives of this study, 51 manageable causes of rework were identified through a comprehensive literature review. According to the nature of BP strategies, the identified IMRCs were classified into three main categories: organization, project, and people. Then, data from 44 case study projects were collected and analyzed. Because information regarding other aspects of these projects, not available through the case study data, was required for this study, a questionnaire was developed and administered to collect additional data about these construction projects. For this purpose, a representative from each project was selected and asked to complete the survey. Finally, 32 significant IMRCs, characterized by 13 attributes, were determined. The project management team (PMT) is typically responsible for preparing the plan, building an effective team, and monitoring the process; therefore, the experience of the PMT in the design and/or construction phase and the number of PMTs who work in a construction project are the most important indicators and contribute the most weight in deriving rework. Next, BPs identified by a construction industry institute were investigated, and among them, 10 BPs were found significantly beneficial for decreasing the cost of rework associated with IMRCs. The implementation of front-end planning determines the feasibility of a project and evaluation of the conceptual scope during the preplanning phase; thus, the design errors due to challenges regarding finance and scope attributes can be decreased with front-end planning. The outcomes of this study may help stakeholders and project managers identify the indicators of rework early in construction projects so they can select the appropriate BP to reduce the costs of rework and improve the project time to completion and cost performance.

Many construction projects suffer from schedule delays that ultimately lead to considerable cost overruns and defeat the purpose of low-cost housing (LCH), which is to support low-income earners. It is, therefore, vital that the schedule delays and cost overruns be minimized. The objectives of this research were to investigate, identify, and classify the schedule-delay indicators (SDIs), prioritize them based on their level of impact, and formulate constructive strategies to improve the schedule performance. To achieve the objectives set forth, 68 interviews were conducted with professionals who are active in LCH projects, and a structured survey was developed and distributed to other experts involved in LCH projects to validate the result of the interviews and collect additional data. Survey responses were collected from 101 individuals and were analyzed. The significant SDIs were identified and classified by the research team and were ranked and prioritized, using the Relative Importance Index (RII) method. The results demonstrated that the identified SDIs could be classified into the following eight main categories: legal, design and technology, project characteristic, project management, material resource, human resource, location, and finance. The outcomes of this study will help project managers and stakeholders identify the causes of schedule delays early in the project and implement effective strategies for improving project performance in low-cost housing projects.

This study aimed to develop predictive models that could be used to estimate the cost and schedule performance of reconstruction of transportation infrastructure damaged by hurricanes and to determine the predictors that are robustly connected to the developed models. Stepwise multiple linear regression and extreme bound analysis (EBA) were used to develop the models and determine the robust and fragile predictors, respectively. The results demonstrated that seven cost performance predictors and nine schedule performance predictors accounted for AdjustedR-Squared of 92.4% and 99.2%, respectively. The results of the EBA revealed that four cost and seven performance predictors were robustly connected to the developed cost and schedule performance predictive models. It was concluded that increases in laborers’ wages, the number of inspections, information and data management, and addressing safety and environmental issues prior to a project’s execution were predictors of both the cost and schedule performance of reconstruction projects. The outcomes of this study provide knowledge and information that will be helpful to decision-makers who are responsible for mitigating delays and cost overruns, and effectively allocating their limited resources available following a disaster.