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.