Competitive bidding typically challenges contractors to stay in business by reducing contingency and limiting profit margin, which imposes more prudent resource utilization and allocation decisions during both planning and construction phases of projects. Many of these decisions must be made considering uncertainties that affect resource production and construction performance through several factors such as weather, managerial practices, job-type, and market conditions, etc. Construction decision makers will therefore have varied approaches to deal with these uncertainties based on their risk utility or perception. This research presents the development of a model for investigating the impact of risk-based approaches on construction network outcomes. The current study contributes to development of a model that enables corporate managers to understand the impact of different resource utilization and sharing policies on the overall outcome of their project and to select among optimum planning solutions that satisfy their profit margin and capital limitations. This research also enables corporate decision makers to have more realistic estimates for the profitability of their company, and understand consequences of their decisions in short and long term. Findings of this research provide decision makers with different solutions for profitability of their corporation based on non-dominated optimal time-cost trade-offs, and also broader perspective on how overall time and budget limitations, as well as risk perceptions, can affect the decision-making process. The model is verified and the results are validated through acquiring data from actual large scale construction projects in South Florida.

The negative impacts associated with highway pavement rehabilitation projects drove state highway agencies (SHAs) towards increased adoption of alternative contracting methods (ACMs) to accelerate the construction of such projects; hence, reducing these impacts on the travelling public. However, the application of such methods showed mixed results due to the lack of specific guidelines addressing the adoption of such methods and the selection of the best ACM for each project. This lack of guidelines stems from the lack of research studies examining the impact of each of these methods on the time/cost trade-off relationship in highway rehabilitation projects. Existing literature includes several studies aimed at developing generic and subjective guidelines based on past experiences that do not take into consideration the unique nature of each of these methods. Hence, this research study aimed at analyzing the SHAs’ decision making process regarding two of the most-widely used ACMs: Incentive/Disincentive (I/D) and Cost + Time (A+B) contracting methods, in order to support decision makers in choosing the most-suitable method for their projects. To this end, two models were developed in this dissertation to examine the time/cost trade-off for each method using simulation and regression analysis. Each model was validated against real-life projects and used to assign appropriate ID and “B” values based on the SHA’s desired duration reduction and available budget. Furthermore, a risk analysis module was developed to determine the most-likely duration reduction that the contractor can achieve for each project under each method. The developed models should help improve the decision making process regarding the selection and implementation of these methods in highway rehabilitation projects. For example, the models can help SHAs identify the minimum ID level that can be offered for each project and the expected duration that the contractors can bid on under the A+B contracting method. Finally, the models were contrasted and applied to real-life projects with different characteristics to verify existing guidelines and establish the candidate ACM for each project category. The findings of this study will benefit the society, SHAs, and the economy in general by optimizing the use of available time and money resources.

Integrated project delivery (IPD) method has recently emerged as an alternative to traditional delivery methods. It has the potential to overcome inefficiencies of traditional delivery methods by enhancing collaboration among project participants. Information and communication technology (ICT) facilitates IPD by effective management, processing and communication of information within and among organizations. While the benefits of IPD, and the role of ICT in realizing them, have been generally acknowledged, the US public construction sector is very slow in adopting IPD. The reasons are - lack of experience and inadequate understanding of IPD in public owner as confirmed by the results of the questionnaire survey conducted under this research study. The public construction sector should be aware of the value of IPD and should know the essentials for effective implementation of IPD principles - especially, they should be cognizant of the opportunities offered by advancements in ICT to realize this. In order to address the need an IPD Readiness Assessment Model (IPD-RAM) was developed in this research study. The model was designed with a goal to determine IPD readiness of a public owner organization considering selected IPD principles, and ICT levels, at which project functions were carried out. Subsequent analysis led to identification of possible improvements in ICTs that have the potential to increase IPD readiness scores. Termed as the gap identification, this process was used to formulate improvement strategies. The model had been applied to six Florida International University (FIU) construction projects (case studies). The results showed that the IPD readiness of the organization was considerably low and several project functions can be improved by using higher and/or advanced level ICT tools and methods. Feedbacks from a focus group comprised of FIU officials and an independent group of experts had been received at various stages of this research and had been utilized during development and implementation of the model. Focus group input was also helpful for validation of the model and its results. It was hoped that the model developed would be useful to construction owner organizations in order to assess their IPD readiness and to identify appropriate ICT improvement strategies.

This research aims to monitor workers’ physiological reactions in virtual construction scenario. With the objective of leveraging affective sensing technology in construction scenario, experiments with Galvanic Skin Response (GSR) was conducted in a 3D simulation developed based on a real construction site. The GSR results obtained from sensor were analyzed in order (i) to assess the feasibility of using virtual environment to generate real emotions, (ii) to examine the relation between questionnaires used to ask people about their experience and their physiological responses and (iii) to identify the factors that affect people’s emotional reactions in virtual environment. Subjects of the experimental group exhibited incoherent responses, as expected in experiments with human subjects. Based on the various reasons for this incoherence obtained from questionnaire part of the experiment, the potential in research for developing training methods with respect to workers’ physiological response capability was identified.

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Non-Destructive Testing (NDT) of deep foundations has become an integral part of the industry’s standard manufacturing processes. It is not unusual for the evaluation of the integrity of the concrete to include the measurement of ultrasonic wave speeds. Numerous methods have been proposed that use the propagation speed of ultrasonic waves to check the integrity of concrete for drilled shaft foundations. All such methods evaluate the integrity of the concrete inside the cage and between the access tubes. The integrity of the concrete outside the cage remains to be considered to determine the location of the border between the concrete and the soil in order to obtain the diameter of the drilled shaft. It is also economic to devise a methodology to obtain the diameter of the drilled shaft using the Cross-Hole Sonic Logging system (CSL). Performing such a methodology using the CSL and following the CSL tests is performed and used to check the integrity of the inside concrete, thus allowing the determination of the drilled shaft diameter without having to set up another NDT device. This proposed new method is based on the installation of galvanized tubes outside the shaft across from each inside tube, and performing the CSL test between the inside and outside tubes. From the performed experimental work a model is developed to evaluate the relationship between the thickness of concrete and the ultrasonic wave properties using signal processing. The experimental results show that there is a direct correlation between concrete thicknesses outside the cage and maximum amplitude of the received signal obtained from frequency domain data. This study demonstrates how this new method to measuring the diameter of drilled shafts during construction using a NDT method overcomes the limitations of currently-used methods. In the other part of study, a new method is proposed to visualize and quantify the extent and location of the defects. It is based on a color change in the frequency amplitude of the signal recorded by the receiver probe in the location of defects and it is called Frequency Tomography Analysis (FTA). Time-domain data is transferred to frequency-domain data of the signals propagated between tubes using Fast Fourier Transform (FFT). Then, distribution of the FTA will be evaluated. This method is employed after CSL has determined the high probability of an anomaly in a given area and is applied to improve location accuracy and to further characterize the feature. The technique has a very good resolution and clarifies the exact depth location of any void or defect through the length of the drilled shaft for the voids inside the cage. The last part of study also evaluates the effect of voids inside and outside the reinforcement cage and corrosion in the longitudinal bars on the strength and axial load capacity of drilled shafts. The objective is to quantify the extent of loss in axial strength and stiffness of drilled shafts due to presence of different types of symmetric voids and corrosion throughout their lengths.

Infrastructure management agencies are facing multiple challenges, including aging infrastructure, reduction in capacity of existing infrastructure, and availability of limited funds. Therefore, decision makers are required to think innovatively and develop inventive ways of using available funds. Maintenance investment decisions are generally made based on physical condition only. It is important to understand that spending money on public infrastructure is synonymous with spending money on people themselves. This also requires consideration of decision parameters, in addition to physical condition, such as strategic importance, socioeconomic contribution and infrastructure utilization. Consideration of multiple decision parameters for infrastructure maintenance investments can be beneficial in case of limited funding. Given this motivation, this dissertation presents a prototype decision support framework to evaluate trade-off, among competing infrastructures, that are candidates for infrastructure maintenance, repair and rehabilitation investments. Decision parameters’ performances measured through various factors are combined to determine the integrated state of an infrastructure using Multi-Attribute Utility Theory (MAUT). The integrated state, cost and benefit estimates of probable maintenance actions are utilized alongside expert opinion to develop transition probability and reward matrices for each probable maintenance action for a particular candidate infrastructure. These matrices are then used as an input to the Markov Decision Process (MDP) for the finite-stage dynamic programming model to perform project (candidate)-level analysis to determine optimized maintenance strategies based on reward maximization. The outcomes of project (candidate)-level analysis are then utilized to perform network-level analysis taking the portfolio management approach to determine a suitable portfolio under budgetary constraints. The major decision support outcomes of the prototype framework include performance trend curves, decision logic maps, and a network-level maintenance investment plan for the upcoming years. The framework has been implemented with a set of bridges considered as a network with the assistance of the Pima County DOT, AZ. It is expected that the concept of this prototype framework can help infrastructure management agencies better manage their available funds for maintenance.

New product development is central to many firms' future success. Not only as a means to continue to maintain their piece of the market, but product development can also be a strategic means for a company to diversify, and/or alter focus to adapt to changing market conditions. Most of the research in new product development has been on how to do it cheaper and faster than the next guy. However, early commercialization does not guarantee a position of strength in the market. Failures of EMI in CT scanners and Xerox in personal computers illustrate that being first to market does not ensure success or even survival. There are two main factors that inhibit managers from making educated decisions on when to introduce a new product. First, firms do not exist in a vacuum and any action they take will be countered by their competition. Second, with new products the only certainty is uncertainty. To allow such decisions to become "gut feeling" decisions puts a company's future at unnecessary risk. This is evidenced by the many firms that have had devastating results because of poor decisions with regard to launching a new product. While high level quantitative tools have recently begun to be used to evaluate corporate strategy, these tools are still mainly confined to research groups within large corporations. Both real options (to handle uncertainty) and game theory (to capture the effects of the competitions actions) have been evaluated and used by these groups. However, they have not been adequately integrated together in the academic world, let alone in industry. This thesis help bridge the gap between strategic decision making, and the theoretical world of economic decision analysis creating a prescriptive model companies can use to evaluate strategically important new product launches. To bridge this gap a method that is able to handle the integration of game-theoretic and options-theoretic reasoning to the strategic analysis of new product introduction is developed. Not only was a method developed that could incorporate the two methods it was done in a way that is accessible and useful outside of the academic world.

Existing office buildings’ embodied energy, history and culture offer something a newly constructed building cannot. On the other hand, new office buildings’ adoption of new technologies and building philosophies offer a range of sustainable efficiencies previously unavailable. Combining these efficiencies with elements that embrace human diversity and well- being offer the opportunity to not only mend our existing buildings’ deteriorating physical bodies but aid in creating workplaces that promote good physical and mental health. This project provides recommendation on how an existing high-rise commercial building can incorporate a number of incremental improvements that continually evolve to meet rapidly changing market demands. This design approach allows for ease of installation and modification to meet the needs of the tenants and the building owner.

<p>The motivation for this research stems from the promise of coupling multi-sensory systems and advanced data analytics to enhance holistic situational awareness and thus prevent fatal accidents in the construction industry. The construction industry is one of the most dangerous industries in the U.S. and worldwide. Occupational Safety and Health Administration (OSHA) reports that the construction sector employs only 5% of the U.S. workforce, but accounts for 21.1% (1,008 deaths) of the total worker fatalities in 2018. The struck-by accident is one of the leading causes and it alone led to 804 fatalities between 2011 and 2015. A critical contributing factor to struck-by accidents is the lack of holistic situational awareness, attributed to the complex and dynamic nature of the construction environment. In the context of construction site safety, situational awareness consists of three progressive levels: perception – to perceive the status of construction entities on the jobsites, comprehension – to understand the ongoing construction activities and interactions among entities, and projection – to predict the future status of entities on the dynamic jobsites. In this dissertation, holistic situational awareness refers to the achievement at all three levels. It is critical because with the absence of holistic situational awareness, construction workers may not be able to correctly recognize the potential hazards and predict the severe consequences, either of which will pose workers in great danger and may result in construction accidents. While existing studies have been successful, at least partially, in improving the perception of real-time states on construction sites such as locations and movements of jobsite entities, they overlook the capability of understanding the jobsite context and predicting entity behavior (i.e., movement) to develop the holistic situational awareness. This presents a missed opportunity to eliminate construction accidents and save hundreds of lives every year. Therefore, there is a critical need for developing holistic situational awareness of the complex and dynamic construction sites by accurately perceiving states of individual entities, understanding the jobsite contexts, and predicting entity movements.<br></p><p>The overarching goal of this research is to minimize the risk of struck-by accidents on construction jobsite by enhancing the holistic situational awareness of the unstructured and dynamic construction environment through a novel data-driven approach. Towards that end, three fundamental knowledge gaps/challenges have been identified and each of them is addressed in a specific objective in this research.<br></p> <p>The first knowledge gap is the lack of methods in fusing heterogeneous data from multimodal sensors to accurately perceive the dynamic states of construction entities. The congested and dynamic nature of construction sites has posed great challenges such as signal interference and line of sight occlusion to a single mode of sensor that is bounded by its own limitation in perceiving the site dynamics. The research hypothesis is that combining data of multimodal sensors that serve as mutual complementation achieves improved accuracy in perceiving dynamic states of construction entities. This research proposes a hybrid framework that leverages vision-based localization and radio-based identification for robust 3D tracking of multiple construction workers. It treats vision-based tracking as the main source to obtain object trajectory and radio-based tracking as a supplementary source for reliable identity information. It was found that fusing visual and radio data increases the overall accuracy from 88% and 87% to 95% and 90% in two experiments respectively for 3D tracking of multiple construction workers, and is more robust with the capability to recover the same entity ID after fragmentation compared to using vision-based approach alone.<br></p> <p>The second knowledge gap is the missing link between entity interaction patterns and diverse activities on the jobsite. With multiple construction workers and equipment co-exist and interact on the jobsite to conduct various activities, it is extremely difficult to automatically recognize ongoing activities only considering the spatial relationship between entities using pre-defined rules, as what has been done in most existing studies. The research hypothesis is that incorporating additional features such as attentional cues better represents entity interactions and advanced deep learning techniques automates the learning of the complex interaction patterns underlying diverse activities. This research proposes a two-step long short-term memory (LSTM) approach to integrate the positional and attentional cues to identify working groups and recognize corresponding group activities. A series of positional and attentional cues are modeled to represent the interactions among entities, and the LSTM network is designed to (1) classify whether two entities belong to the same group, and (2) recognize the activities they are involved in. It was found that by leveraging both positional and attentional cues, the accuracy increases from 85% to 95% compared with cases using positional cues alone. Moreover, dividing the group activity recognition task into a two-step cascading process improves the precision and recall rates of specific activities by about 3%-12% compared to simply conducting a one-step activity recognition.<br></p> <p>The third knowledge gap is the non-determining role of jobsite context on entity movements. Worker behavior on a construction site is goal-based and purposeful, motivated and influenced by the jobsite context including their involved activities and the status of other entities. Construction workers constantly adjust their movements in the unstructured and dynamic workspace, making it challenging to reliably predict worker trajectory only considering their previous movement patterns. The research hypothesis is that combining the movement patterns of the target entity with the jobsite context more accurately predicts the trajectory of the entity. This research proposes a context-augmented LSTM method, which incorporates both individual movement and workplace contextual information, for better trajectory prediction. Contextual information regarding movements of neighboring entities, working group information, and potential destination information is concatenated with movements of the target entity and fed into an LSTM network with an encoder-decoder architecture to predict trajectory over multiple time steps. It was found that integrating contextual information with target movement information can result in a smaller final displacement error compared to that obtained only considering the previous movement, especially when the length of prediction is longer than the length of observation. Insights are also provided on the selection of appropriate methods.<br></p><p>The results and findings of this dissertation will augment the holistic situational awareness of site entities in an automatic way and enable them to have a better understanding of the ongoing jobsite context and a more accurate prediction of future states, which in turn allows the proactive detection of any potential collisions.<br></p>