Data-Driven Analysis Of Construction Bidding Stage-Related Causes Of Disputes

Construction bidding is a complex process that involves several potential risks and uncertainties for all the stakeholders involved. Such complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims, conflicts, and disputes during the course of a project. Even though a substantial amount of knowledge has been acquired about construction disputes and their causation, there is a lack of research that examines the causes of disputes associated with the bidding phase of projects. This study addresses this knowledge gap within the context of infrastructure projects. In investigating and analyzing the causation of disputes related to the bidding stage, the authors implemented a multistep research methodology that incorporated data collection, network analysis (NA), spectral clustering, and association rule analysis (ARA). Based on a manual content analysis of 94 legal cases, the authors identified a comprehensive list of 27 causes of disputes associated with the bidding stage of infrastructure projects. The NA results indicated that the major common causes leading to disputes in infrastructure projects comprise inaccurate cost estimates, inappropriate tender documents, nonproper or untimely notification of errors in a submitted bid, nonproper or untimely notification of errors in tender documents, and noncompliance with Request for Proposals\u27 (RFP) requirements. Upon categorizing and clustering the causes of disputes, the ARA results revealed that the most critical associations are related to differing site conditions, errors in submitted bids, unbalanced bidding, errors in cost estimates, and errors in tender documents. This study promotes an in-depth understanding of the causes of disputes associated with the bidding phase within the context of infrastructure projects, which should better enable the establishment of proactive plans and practices to control these causes as well as mitigate the occurrence of their associated disputes during project execution

Management of Change Orders in Infrastructure Transportation Projects

The Illinois Department of Transportation (IDOT) will handle many upcoming projects due to the recent statewide infrastructure strategic plan and the fast-track efforts affecting many infrastructure projects amid COVID-19. Nevertheless, many change orders are anticipated to occur on IDOT\u27s projects. Thus, this paper examines the proper contractual management of changes within IDOT infrastructure transportation projects by following a research method based on the integration between a desktop analysis and a focus group analysis. The desktop analysis involved collecting information and data from existing resources, case studies, and documents related to change orders. The focus group analysis involved consulting with change order experts to verify that the outcome of each research step is useful and to validate the final outcomes of the paper. Based on 50 documented major change orders in IDOT projects and three litigated cases, two findings are provided. First, the top causes for key change orders within IDOT projects include contract administration, allowable contingencies, quantity omission or error, differing site conditions, and design changes. Second, the most critical change order related challenges within IDOT\u27s infrastructure projects include approval procedures, compensation considerations, and applicable laws. This paper offers flowcharts, synopsis of opportunities and risks, and a checklist to help the contracting parties better administer change orders. Ultimately, the contributions of this paper to the practice include: (1) minimizing the number and amount of change orders, (2) helping the contracting parties better understand how their individual responsibilities contribute to the proper processing and management of changes and variations, (3) offering contractors the ability to visualize the different steps involved in the approval of change orders, (4) assisting the project stakeholders in identifying change order-related areas for improvement, and (5) allowing project owners to better mitigate, manage, and administer the contractual aspects of change orders

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

An Integrated Game-Theoretic and Reinforcement Learning Modeling for Multi-Stage Construction and Infrastructure Bidding

Construction and infrastructure bidding is a highly competitive and complicated process that entails various uncertainties faced by contractors. The situation is more complex in multi-stage bidding (MSG) where general contractors must deal with the complexity of accounting for the bids of their subcontractors and face a greater threat of falling prey to the winner\u27s curse (i.e. situation where the winning contractor underestimates the actual cost of the project). Existing research efforts have tackled the issue of the winner\u27s curse in MSG from the general contractor\u27s perspective. However, there is a lack of research in developing bidding models that simultaneously aid both general contractors and subcontractors in determining their bid value to mitigate the winner\u27s curse in MSG. This paper fills this knowledge gap. The authors utilized an interdependent game theory (GT) and reinforcement learning (RL) approach, that includes: formulation of MSG framework; incorporation of two RL algorithms, namely the multiplicative weights and the modified Roth-Erev, to be utilized by subcontractors in preparation of their bids; utilization of MSG game-theoretic bid function for the preparation of the general contractors\u27 bids for the whole project; development of the MSG-GTRL model; and testing the MSG-GTRL model through simulating various bidding scenarios using a combination of actual and synthetic dataset of infrastructure projects. Results show that integrating GT and RL in MSG bidding enables general contractors and their subcontractors to simultaneously improve their financial state by minimizing the occurrence of negative earnings, and thus, avoiding the winner\u27s curse in their respective portions of projects

Construction Research Congress 2022

The global and national spending on public infrastructure projects continues to increase tremendously. Accordingly, contractors need to adopt efficient bidding strategies to cope with the legal requirement of competitive bidding within the public infrastructure projects. Moreover, contractors usually acquire the services of subcontractors to handle large projects. However, despite the previous research efforts on developing bidding models, there is still a lack of research that tackles the multi-stage construction bidding, where subcontractors submit quotations/bids first and general contractors bid second for the whole project. As such, this paper aims to develop a game-theoretic bidding model for multi-stage construction bidding. To this end, the authors utilized an interrelated methodology comprised of: (1) investigating existing bidding models that are based on a game theory approach; (2) deriving of a bid function for the multi-stage construction bidding following the low bid method where the lowest bidder is the winner; (3) simulating multi-stage bidding environment; and (4) validating the derived bid function and simulation model utilizing data of 808 US public infrastructure projects. Results indicate that the derived bid function gives general contractors a competitive advantage by reducing the occurrence and magnitude of earning negative profits (known as the winner\u27s curse) while winning a reasonable number of projects; and hence, resulting in a higher expected profit. Ultimately, this study adds to the body of knowledge by providing a bidding model for the multi-stage construction bidding that shall aid contractors in dealing with the uncertainties within the associated decision-making process

Solving the Negative Earnings Dilemma of Multistage Bidding in Public Construction and Infrastructure Projects: A Game Theory-Based Approach

With the tremendous increase in spending on public projects, contractors need to employ efficient and effective bidding strategies to cope with the competitive bidding environment. Usually, general contractors carry a portion of the work and subcontract other parts to eventually submit a holistic joint bid. This bidding setting is referred to as multistage bidding where subcontractors submit their quotations/bids to the general contractor, after which the general contractor submits a final joint bid for the whole project. In a multistage bidding environment, general contractors may be faced with an increase in the probability of negative or below normal profits. Despite previous research efforts for developing bidding models, there is a need for the extension of existing literature to tackle the multistage bidding environment, referred to hereinafter as multistage game (MSG). As such, the goal of this paper is to develop a bidding model for the MSG. The authors followed a multistep research methodology comprised of: (1) defining MSG in terms of game theory; (2) deriving a game-theoretic bid function for general contractors to determine the final joint bid to submit in MSG; and (3) developing a simulation model for MSG, using a data from 2,235 US public infrastructure projects. Results demonstrate that the new bid function gives general contractors a competitive advantage by avoiding the occurrence of negative profits in their part of the project. Also, results show a reduction in the occurrence and magnitude of the negative profits in relation to the final joint bids. This research significantly contributes to the body of knowledge by providing an innovative bid function for MSG. In addition, it offers substantial practical benefits for general contractors by providing a tool that facilitates dealing with the inherent complexity and uncertainties related to actual cost estimation within the MSG decision-making process

Multi-stage bidding for construction contracts : a game theory approach

In the construction industry, auctions have long been used as a method for allocating contracts. Not addressed in the literature (engineering or economics) is the fact that most, if not all, large jobs are awarded to a general contractor who in turn sub-contracts most, if not all, actual engineering services. Optimal bidding strategies in this setting require the general contractor to not only account for the optimal bidding strategies of rivals, but sub-contractors as well. Because the true cost of construction is not known until after the completion of the contract, adverse selection occurs when the winner of the contract is the one that most has under estimated the true cost. Due to the multi-stage bidding environment, adverse selection may be compounded. Therefore, not accounting for the potential for adverse selection by bidders may result in requested change orders by the general and sub-contractors or lower quality services. Either state ultimately results in an adversarial relationship between the sub-contractor and general contractor, and the client as well. This paper uses game theory to determine to what extent the multi-stage aspects of large construction contract bidding may contribute to inefficient allocation of contracts. This should better help in creating an efficient and effective contracting environment that result in less conflicts, claims and disputes for all the associated stakeholders.Non UBCUnreviewedFacultyOthe

Evaluating Deterioration of Tunnels using Computational Machine Learning Algorithms

Tunnels are an integrated part of the transportation infrastructure. Structural evaluation and inspection of tunnels are vital tasks to assess the deterioration of tunnels and maintain their level of service. Researchers have developed many predictive models that describe the deterioration of various infrastructure systems using data from formal inspections. However, there is a lack of research that developed predictive models of deterioration of tunnels in the US. Therefore, this paper investigated the feasibility of using various machine learning techniques to develop a computational data-driven decision support tool that predicts the deterioration of tunnels in the US. An ex ante framework for predicting the deterioration of tunnels in the US was developed. The research methodology comprised (1) collecting, cleaning, and standardizing data for tunnels in the US from the Federal Highway Administration (FHWA); (2) identifying the best subset of variables that allow predicting the deterioration of tunnels; (3) utilizing existing machine learning algorithms, namely k-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and support vector machine (SVM), to develop classification models that predict the deterioration of tunnels; (4) optimizing the accuracy of the developed models by determining the best set of hyperparameters that result in the most accurate performance; (5) comparing the performance of the developed models and selecting the best performing model to be used as a decision support tool; and (6) evaluating and validating the performance of the selected model. The results identified 18 variables that greatly affect the deterioration of tunnels, with the tunnel width having the greatest impact on the prediction of deterioration of tunnels. Results indicated that the RF algorithm reached an accuracy of 85.38%, which was the highest accuracy, compared with KNN, ANN, and SVM, which reached an accuracy of 80.12%, 56.14%, and 56.73%, respectively. In addition, the entropy criterion function with a maximum of five features and 500 estimators successfully constructed the best hyperparameters for the selected RF model. Therefore, the developed decision support tool can be used by transportation entities to estimate the overall condition of tunnels based on specific tunnel parameters with reasonable prediction accuracy. It also can aid decision makers in developing, optimizing, and prioritizing maintenance plans and allocation of funding

Management of Change Orders in Infrastructure Transportation Projects

The Illinois Department of Transportation (IDOT) will handle many upcoming projects due to the recent statewide infrastructure strategic plan and the fast-track efforts affecting many infrastructure projects amid COVID-19. Nevertheless, many change orders are anticipated to occur on IDOT\u27s projects. Thus, this paper examines the proper contractual management of changes within IDOT infrastructure transportation projects by following a research method based on the integration between a desktop analysis and a focus group analysis. The desktop analysis involved collecting information and data from existing resources, case studies, and documents related to change orders. The focus group analysis involved consulting with change order experts to verify that the outcome of each research step is useful and to validate the final outcomes of the paper. Based on 50 documented major change orders in IDOT projects and three litigated cases, two findings are provided. First, the top causes for key change orders within IDOT projects include contract administration, allowable contingencies, quantity omission or error, differing site conditions, and design changes. Second, the most critical change order related challenges within IDOT\u27s infrastructure projects include approval procedures, compensation considerations, and applicable laws. This paper offers flowcharts, synopsis of opportunities and risks, and a checklist to help the contracting parties better administer change orders. Ultimately, the contributions of this paper to the practice include: (1) minimizing the number and amount of change orders, (2) helping the contracting parties better understand how their individual responsibilities contribute to the proper processing and management of changes and variations, (3) offering contractors the ability to visualize the different steps involved in the approval of change orders, (4) assisting the project stakeholders in identifying change order-related areas for improvement, and (5) allowing project owners to better mitigate, manage, and administer the contractual aspects of change orders

Understanding the Construction Winner\u27s Curse using Game Theory

The winner\u27s curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project. This paper uses game theory to identify the degree of the winner\u27s curse in two common construction bidding environments; namely, single-stage bidding and multi-stage bidding. The objective is to compare the aforementioned two construction bidding environments, and determine how learning from past bidding decisions and experiences can mitigate from the winner\u27s curse. To this end, the authors (1) presented the symmetric risk neutral Nash equilibrium (SRNNE) as an optimal bid function; (2) developed simulation models for single and multi-stage construction bidding processes; and (3) analyzed the results of the simulation models, which is based on an actual dataset of California Department of Transportation projects. This research demonstrated that the majority of general contractors and sub-contractors suffer from the winner\u27s curse in both single and multi-stage bidding environments, and that the SRNNE optimal bid function provides the contractors with a tool to avoid the winner\u27s curse problem and to consequently gain strategic positive profits in both bidding environments. This research should reduce the industry exposure to the effects of the winner\u27s curse in construction bidding