The time-cost trade-off problem (TCTP) is fundamental to project scheduling. Risks in estimation of project cost and duration are significant due to uncertainty. This uncertainty cannot be eliminated by any scheduling or estimation techniques. Therefore, a model that can represent uncertainty in the real world to solve time-cost trade-off problems is needed. In this chapter, fuzzy logic is utilized to consider affecting uncertainties in project duration and cost. An optimization algorithm based on time-driven activitybased costing (TDABC) is applied to provide a trade-off between project time and cost. The presented model could solve the time-cost trade-off problem while accounting for uncertainty in project cost and duration. This could help generate a more reliable schedule and mitigate the risk of projects running overbudget or behind schedule.

As is often the case in project scheduling, when the project duration is shortened to decrease total cost, the total float is lost resulting in added critical or nearly critical activities. This, in turn, results in decreasing the probability of completing the project on time and increases the risk of schedule delays. To solve this problem, this research developed a fuzzy multicriteria decision-making (FMCDM) model. The objective of this model is to help project managers improve their decisions regarding time-cost-risk trade-offs (TCRTO) in construction projects. In this model, an optimization algorithm based on fuzzy logic and analytic hierarchy process (AHP) has been used to analyze the time-cost-risk trade-off alternatives and select the best one based on selected criteria. The algorithm was implemented in the MATLAB software and applied to two case studies to verify and validate the presented model. The presented FMCDM model could help produce a more reliable schedule and mitigate the risk of projects running overbudget or behind schedule. Further, this model is a powerful decision-making instrument to help managers reduce uncertainties and improve the accuracy of time-cost-risk trade-offs. The presented FMCDM model employed fuzzy linguistic terms, which provide decision-makers with the opportunity to give their judgments as intervals comparing to fixed value judgments. In conclusion, the presented FMCDM model has high robustness, and it is an attractive alternative to the traditional methods to solve the time-cost-risk trade-off problem in construction.

Infrastructure vulnerability has drawn significant attention in recent years, partly because of the occurrence of low-probability and high-consequence disruptive events such as 2017 hurricanes Harvey, Irma, and Maria, 2011 Tuscaloosa and Joplin tornadoes, and 2015 Gorkha, Nepal, and 2017 Central Mexico earthquakes. Civil infrastructure systems support social welfare, thus viability and sustained operation is critical. A variety of frameworks, models, and tools exist for advancing infrastructure vulnerability research. Nevertheless, providing accurate vulnerability measurement remains challenging. This paper presents a state-of-the-art data collection and information extraction methodology to document infrastructure at high granularity to assess preevent vulnerability and postevent damage in the face of disasters. The methods establish a baseline of preevent infrastructure functionality that can be used to measure impacts and temporal recovery following a disaster. The Extreme Events Web Viewer (EEWV) presented as part of the methodology is a GIS-based web repository storing spatial and temporal data describing communities before and after disasters and facilitating data analysis techniques. This web platform can store multiple geolocated data formats including photographs and 360° videos. A tool for automated extraction of photography from 360° video data at locations of interest specified in the EEWV was created to streamline data utility. The extracted imagery provides a manageable data set to efficiently document characteristics of the built and natural environment. The methodology was tested to locate buildings vulnerable to flood and storm surge on Dauphin Island, Alabama. Approximately 1,950 buildings were passively documented with vehicle-mounted 360° video. Extracted building images were used to train a deep learning neural network to predict whether a building was elevated or nonelevated. The model was validated, and methods for iterative neural network training are described. The methodology, from rapidly collecting large passive datasets, storing the data in an open repository, extracting manageable datasets, and obtaining information from data through deep learning, will facilitate vulnerability and postdisaster analyses as well as longitudinal recovery measurement.

The hazardous nature of the construction environment and current incident statistics indicate a pressing need for safety performance improvement. One potential approach is the strategic analysis of leading indicators for measuring safety performance as opposed to using only lagging indicators, which has protractedly been the norm. This study presents a systematic safety performance measurement framework and statistical modeling processes for analyzing safety incident data for accident prediction and prevention on construction sites. Using safety incident data obtained from a construction corporation that implements proactive safety management programs, statistical modeling processes are utilized to identify variables with high correlations of events and incidents that pose dangers to the safety and health of workers on construction sites. The findings of the study generated insights into the different types and impacts of incident causal factors and precursors on injuries and accidents on construction sites. One of the key contributions of this study is the promotion of proactive methods for improving safety performance on construction sites. The framework and statistical models developed in this study can be used to collect and analyze safety data to provide trends in safety performance, set improvement targets, and provide continuous feedback to enhance safety performance on construction sites.

One of the difficulties hindering the application of 3D printing technology in construction is related to the versatility of materials and components used to produce a building or other structure. The prospect of using this technology is further complicated by the sheer size of the edifices to be constructed. While 3D printing a mechanical component can now be done in someone's basement with affordable and readily available equipment, applying the same technology to produce large structures and building components is a challenge. In recent years, researchers have been working towards overcoming this challenge by trying to develop new construction materials and methods that would be more suitable for the application of 3D printing technology. One of the approaches that can be considered is the combination of robotics technology with 3D printing to automate construction activities. The use of robots in construction has been proposed long before 3D printing became possible or known but never gained widespread construction site usage, mainly because of the difficulty associated with the automation of most construction tasks. However, the combination of 3D printing with robotics may be the way to change that. In this paper, the authors examine the suitability of 3D printing in a number of construction tasks and present ideas that modify established construction methods to make them more suitable for automation. The authors then examine how the introduction of robotics in conjunction with 3D printing to the construction site may make it possible to automate a number of construction tasks. Some of the benefits of such automation include lower safety risks, improved control over construction schedules, more economical construction, and a better ability to build in remote areas and challenging environments. © Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.

The advent of 3D printing technology may very well be remembered as one of the most important technological advances of the early twenty-first century. This technology is transforming the dynamics of the manufacturing world in ways that may have not been thought possible a couple of decades ago. 3D printing is now being used in medicine and dentistry to make prosthetic parts, sensors, and medical models among a number of other applications. The versatility of the types of materials that can be 3D printed makes the process extremely useful. The technology is being used in different industries to produce various parts and components for generally lower costs while achieving a better quality. This is either achieved by 3D printing the parts themselves or the molds that would eventually be used to make the parts. However, the construction industry has been slow in adopting this technology for many reasons, many of which still need to be investigated so a way can be found around them. In this paper the authors first examine the history of 3D printing applications in the construction industry. They then provide an overview of recent attempts at applying the technology while discussing the successes and challenges encountered. They finally propose solutions for resolving some of the identified challenges to help the industry move forward in taking advantage of this emerging and potentially beneficial technology.

PurposeRisk impedes the success of construction projects in developing countries due to planning in an unpredictable and poorly resourced environment. Hence, the literature suggests that practitioners are not fully aware of how important the risk identification process is. Some of the prior studies identified risks in developing countries without highlighting how they can be beneficial to the practitioners in the industry. Therefore, this study highlights this process and identifies the key risks that affect the Jordanian construction industry.Design/methodology/approachThis study adopted an exploratory sequential mixed approach, two rounds of face-to-face interviews that were conducted in Jordan among 12 experts followed by a questionnaire randomly distributed to 122 practitioners. This study utilized the relative importance index, coefficient of variation, and Mann–Whitney (U) to analyze the data. Also, the factor analysis technique was used to identify and regroup the risk factors to further understand the correlation among the risks.FindingsThe result revealed an agreement among contractors’ and consultants’ responses toward allocating risks. Furthermore, several risks can be traced back to the project communication management process, highlighting a deficiency in the process. Also, four-factor groups were established, the first group includes the risk of defective design, late decisions making by other project participants and poor coordination with the subcontractor. The second group has only the risk of corruption, including bribery at sites. The third group includes stakeholders’ financial instability and inadequate distribution of responsibilities and risks. The fourth group includes adverse weather conditions and the use of illegal foreign labor.Originality/valueSome of the prior studies identified risks in developing countries without highlighting how they can be beneficial to the practitioners in the industry.

With the BIM applications on fast-track, it is being used for new construction and recently being utilized for renovation, retrofitting, and repairing purposes. Although many studies have been conducted to identify BIM capabilities in renovation projects, the application of 4D BIM for both the demolition and the construction phases of renovation projects is still underdeveloped. This paper presents a guideline to apply 4D BIM for complicated renovation projects that include demolition and construction phases. The paper represents a step-by-step process from the extraction of information from a 2D model and converting it into a 4D model. The proposed guideline will help CAD users to apply 4D BIM for complicated renovation model. A real case application is analyzed to demonstrate the potential of the presented guideline. The research results show that the proposed guideline could assist in construction management by finding out inappropriate sequences in schedule, conducting evaluation of issues related to constructability, and identifying disagreements in time and space. The proposed guideline could help identify errors in construction scheduling, which have the potential to reduce project cost and duration.