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Project bundling has been introduced as an innovative approach that groups several infrastructure projects into a single contract. Project bundling has numerous benefits such as expediting project delivery, capitalizing on economies of scale, and reducing the overall contract cost. However, agencies implementing project bundling also face some challenges, one of which is assessing the feasibility of a certain project delivery method (PDM) of interest. While different PDMs could be used for bundled projects, design-build (DB) has attracted particular interest from project owners as an alternative, innovative PDM due to its numerous benefits related to project performance. While previous research efforts examined different aspects of project bundling in terms of its decision-making factors, opportunities, and challenges, no previous research effort was devoted to studying the feasibility or applicability of the DB delivery method for bundled projects. To that extent, and to fill this gap, this paper develops a scoring framework that assesses the feasibility of the DB delivery for bundled projects. First, a questionnaire survey was distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis (PCA) was implemented to calculate a weight for each project bundling objective. Third, a score for each project bundling objective was calculated and the DB feasibility tiers were identified using a series of statistical tests. Finally, a user-friendly feasibility scoring framework was developed to assign a feasibility score for the DB delivery method based on the user's inputs related to the bundled project of interest. The results showed that the key project bundling objectives that mostly increase the feasibility score of the DB delivery method for bundled projects are (1) Having specific scheduling, sequencing, staging, and phasing; (2) Meeting tight milestones or deadlines (timely completion of individual phases); and (3) Completing the overall project on schedule. Ultimately, this research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them make the go/no-go decision for the DB delivery method for their bundled projects. By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM selection), reduce the associated risks and challenges, improve the overall schedule of the bundled program, and keep the cost within the identified budgets. [ABSTRACT FROM AUTHOR]

Amid escalating global challenges such as population growth, pollution, and climate change, access to safe and clean water has emerged as a critical issue. It is estimated that there are 4 billion cases of water-related diseases worldwide that cause 3.4 million deaths every year. This underscores the urgent need for efficient water quality monitoring and assessment. Traditional assessment techniques include laboratory-based methods that are manual, costly, time-consuming, and risky. Although some studies leveraged Internet of Things (IoT)-based systems to examine water quality, they only relied on a limited number of water quality parameters (and thus do not offer a comprehensive and accurate water quality assessment), mainly due to the technical difficulties to integrate multiple sensors to a single device. In fact, due to the issues of multimodality, heterogeneity, and complexity of data, the interoperability among sensors with various measurements, sampling rates, and technical requirements makes it very challenging to seamlessly integrate multiple sensors into one device. This study overcame these technical challenges by leveraging multisensor data fusion capabilities to develop an intelligent cloud-based IoT multimodal edge sensing device to provide an automated, real-time, and comprehensive assessment process of water quality. First, a total of nine water quality parameters were identified and considered. Second, the sensing device was designed and developed using an ESP32 embedded system, which is a low-cost, low-power system on a chip (SoC) microcontroller integrated with Wi-Fi and dual-mode Bluetooth connectivity by fusing data from six different sensors that measure the nine identified water parameters on the edge. Third, the overall water quality was evaluated using the National Sanitation Foundation Water Quality Index (NSFWQI). Fourth, a cloud-based server was created to publish the data instantly, and a graphical user interface (GUI) was developed to provide easy-to-understand real-time visualization and information of the water quality. The real-world applicability and practicality of the developed IoT-enabled sensing device was tested and verified in a pilot project (i.e., a case study) of a building located in Newark, New Jersey, for a duration of 6 months. This paper adds to the body of knowledge by being the first research developing a single IoT-enabled device that is capable of reporting NSFWQI in real-time based on 9 water quality indicators encompassing both physical [temperature, total dissolved solids (TDS), turbidity, and pH] and chemical [potassium, phosphorus, nitrogen, dissolved oxygen (DO), and 5-day biochemical oxygen demand (BOD5)] parameters. Thus, this study serves as a multifaceted improvement across different dimensions, fostering healthier, more efficient, and technologically advanced environments. [ABSTRACT FROM AUTHOR]

Project bundling is an innovative project-delivery approach that bundles and groups different infrastructure projects into a single contract. Project bundling provides numerous opportunities and benefits for agencies and states while maintaining, rehabilitating, and repairing their infrastructure assets. Although previous research provided insights on some aspects of project bundling, none have identified the success factors needed to enhance the performance of bundled projects after the project bundling decision has been made. This study aims to fill this gap through identifying, studying, and prioritizing the success factors needed to efficiently bundle several infrastructure projects together. First, a survey was developed and distributed to numerous experts to examine the likelihood of occurrence and relative impact of 25 success factors. Second, the reliability of the survey and its results were assessed and validated using internal and external reliability assessment measures. Third, various statistical analysis tests were performed to examine the alignment between the perspectives of different project stakeholders in relation to the various project-bundling success factors. Fourth, the criticality of the different project-bundling success factors was quantified. Fifth, the success factors were ranked and prioritized to identify the key and most critical factors. The results showed that the most critical project-bundling success factors are (1) having well-defined design features; (2) having a complete project design prior to construction; (3) requiring prior knowledge or experience with similar project size and scope (by the contractor and the design agency); (4) having clear design control requirements; and (5) maintaining constant coordination and communication between all project stakeholders. This study adds to the body of knowledge by quantifying and studying the criticality of different success factors needed to enhance the performance of bundled projects. This study also provides decision-makers with the required knowledge and guidance on the key success factors that influence project-bundling performance. [ABSTRACT FROM AUTHOR]

Construction firms face considerable challenges in relation to finding cost-effective formwork solutions to meet increased construction demands. Project stakeholders have relied on self-consolidating concrete (SCC) to speed up the construction time because SCC is highly fluid and has numerous advantages compared to traditional concrete. To withstand SCC's high fluidity, formwork systems should be robust. Although previous research has experimentally examined various characteristics of SCC, few research studies have used machine-learning algorithms to estimate or predict the lateral pressure exerted by SCC on formwork systems. Hence, this study addressed this knowledge gap by proposing a machine-learning approach to predict the lateral pressure of SCC on vertical formwork systems. First, laboratory tests were performed to collect data on lateral pressure measurements, material factors, placement conditions, and formwork characteristics affecting the SCC lateral pressure on formwork systems. Second, four supervised machine-learning algorithms were considered in this study: k-nearest neighbor (KNN), artificial neural network (ANN), decision tree (DT), and random forest (RF). Third, the hyperparameters of the machine-learning algorithms were tuned, and their performance metrics were compared. Fourth, the most accurate predictive machine-learning model was verified on an unseen testing set. The results showed that the RF machine-learning algorithm was the best model for predicting the lateral pressure of SCC on formwork systems, with a mean percentage error of 0.8%, a mean absolute percentage error of 4.29%, and a coefficient of determination R2 of 0.9548. This study adds to the construction engineering and management body of knowledge by developing a machine-learning predictive model that can be used to accurately assess the lateral pressure exerted by SCC on formwork, which helps to ensure safe design of formwork systems and economic construction operations in formwork-related activities. [ABSTRACT FROM AUTHOR]

The construction industry has witnessed unprecedented disruptions in the supply chain (SC) over the past four years due to the consecutive impacts of the 2018 steel and aluminum tariffs, the COVID-19 pandemic, and the Russian–Ukrainian war. SC disruptions have been linked to price escalations, the management of which varies across different construction contracts. Thus, there is a timely need to understand the contractual implications of price escalation clauses. This article fulfills this research requirement by following an interdependent research methodology. First, the authors analyzed and compared price escalation provisions under various US-based, international, and UK-based standard design–bid–build contracts. Second, the authors examined legal cases that have arisen due to disputes over price escalation-related matters. Third, based on the contractual and legal analysis, the authors formulated and proposed a set of contractual considerations that can be used to plan and manage the contractual implications arising from triggering price escalation clauses. The findings indicate substantial variability in contractual provisions addressing price escalation. A predominant approach entails specifying a fixed contract price, often with a general exception permitting adjustments related to legislative changes. Certain standard contracts afford flexibility through the incorporation of supplementary clauses or amendments during the contract formation stage to counteract escalation risks. Further, insights from the legal review show that judicial relief from contractual obligations is typically granted solely in instances of unforeseen and severe economic conditions. This research makes a significant academic contribution by advancing the understanding of contractual obligations concerning price escalation, especially in the context of ongoing market disruptions. Ultimately, the findings of this study will impact the construction industry by promoting more balanced contractual practices regarding price escalations in construction projects. [ABSTRACT FROM AUTHOR]

Project bundling is an innovative project delivery approach that combines several projects under a single contract. While previous studies have provided important information about different project bundling-related aspects, none have developed guidelines to choosing the best project delivery method (PDM) for bundled projects/contracts. Also, despite that some of the existing research efforts have offered tools to identify the optimal PDM, such studies were conducted for single projects rather than for bundled projects which significantly differ from a normal project in terms of complexity and implementation considerations. Hence, this paper develops a data-driven decision support tool that helps agencies in identifying the optimal PDM for their bundled projects by leveraging machine learning algorithms and domain knowledge while also considering the characteristics and goals of the bundled program. This proposed tool considers and compares the following 5 PDMs: design bid build (DBB); design build (DB); construction manager/general contractor (CM/GC); indefinite delivery/indefinite quantity (IDIQ); and public private partnership (PPP). First, data from previous project bundling case studies were used to identify bundling opportunities (on the program or strategic level) as well as bundling objectives (on the project or contract level). Second, a machine learning model (i.e., multinomial naïve Bayes classifier) was developed to generate a probabilistic distribution for the relative suitability of the five PDMs on the strategic bundling program level. Third, a survey was developed and distributed to collect expert's domain knowledge on the importance of the different project bundling objectives (i.e., on the project or contract level). Lastly, an easy-to-use decision-support tool was developed to calculate individual scores for the different 5 PDMs so that the best PDM could be identified. Ultimately, this paper presents an intuitive and easy to implement tool for selecting PDMs for bundled projects based on the integration of machine learning algorithms and domain knowledge. Practical Applications: This paper provides numerous practical applications. More specifically, this research equips project owners with a data-driven tool to select the optimal PDM for their bundled projects by considering factors and characteristics on the overall program level as well as on the project level. This tool also helps in properly allocating risks between the project parties or stakeholders involved in the delivery of bundled projects. The developed tool in this paper is integrated into user-friendly excel spreadsheets with automated calculations, and thus it automates the PDM-related decision-making process, which makes it easy to implement in practice. Consequently, by selecting the optimal PDM, the proposed decision-support tool would enable project owners to enhance their bundling practices, improve the performance of their bundled program, maintain costs within identified budgets, improve the schedule of their bundled projects, and better capitalize on the various benefits of the bundling approach. [ABSTRACT FROM AUTHOR]

While green infrastructures (GIs) are used to address urban flooding and stormwater runoff and enhance sustainability, their implementation faces challenges such as limited funding and competing priorities. Existing studies generally focused on specific/individual GI practices, potentially overlooking the broader benefits or values of comprehensive GI plans that generally are interconnected networks of multiple GI practices. Thus, this paper developed an agent-based model to offer municipalities, planners, and decision makers a structured and systematic modeling approach to: (1) simulate the dynamic nature of GI implementation plans across spatial and temporal dimensions; (2) capture the interplay among regulations that govern GI plans; and (3) conduct scenario and sensitivity analyses to gain deeper insights into the various characteristics of GI plans under different scenarios. The proposed model considered different economic values of GI plans, including stormwater management, carbon sequestration, pollutant removal, energy cost savings, and increased property value, as well as the social values, such as the positive impact on nearby residents/communities. In addition, the developed model considered the following six GI practices: green roofs, rain gardens, community gardens, bioswales, permeable playgrounds, and permeable pavements. These GI practices were modeled across five submodels: school greening, private property greening, park greening, right-of-way property greening, and public housing properties greening. Scenario analysis was conducted to investigate the effect of financial limitations (i.e., funding priorities) on the model's behavior under two scenarios. Finally, the developed model was statistically validated. The results showed that increasing the number of GIs does not guarantee an increase in their economic value, but rather the implementation of GIs should be well planned according to a priority plan. The findings also provided valuable insights into the importance of considering physical and institutional limitations. This paper contributes a practical workflow for evaluating the costs and benefits of GI programs. [ABSTRACT FROM AUTHOR]

The level of preparedness of communities to heat-related consequences varies. While several studies have developed heat vulnerability indices, little-to-no research efforts were directed to assess the heat resilience of communities. To that extent, this paper develops and validates a heat resilience index (HRI). First, a dataset of 44 indicators affecting heat resilience was collected and grouped into five categories: sociodemographic, land use/land cover (LULC), health facilities, meteorological, and geographic factors. Second, principal component analysis (PCA) was used to calculate the weights or importance of each indicator. Based on the calculated weights, five heat resilience subindices were developed for the five categories. Third, the overall HRI was developed as a weighted average of the five calculated subindices. Fourth, the developed HRI was scientifically validated based on real-world heat-related illnesses data. The HRI was demonstrated for the State of New Jersey, where the results showed that more than 71% of the studied census tracts have a poor resilience toward heat waves. Furthermore, the results highlighted that the following indicators affect heat resilience the most: median income, poverty, percentage of people younger than 5 years old, land area, building area, annual Normalized Difference Vegetation Index (NDVI), summer NDVI, number of hospitals, mean annual temperature, minimum temperature, maximum temperature, and urban elevation. Also, the results identified the LULC category as the most influential category on the overall heat resilience. The findings could assist in developing appropriate heat management and mitigation plans to enhance communities' ability to resist future heat waves, where the same method could be used to assess heat resilience for other states and countries worldwide through developing the associated dataset. Ultimately, this research adds to the body of knowledge by proposing a structured framework for developing a novel heat resilience index based on a comprehensive list of 44 indicators. [ABSTRACT FROM AUTHOR]

The partnership between the public and private sectors has led to a new and innovative way of delivering infrastructure projects that is referred to as public–private partnership (PPP). There are various benefits associated with PPP delivery methods including risk sharing, access to private funding, innovation, and flexibility, among others. Despite the proved benefits, contract conflicts and disputes are very common in PPP projects. While previous research studies examined the risks and the potential causes of conflicts in PPP projects, little-to-no research efforts were directed to study and model the interconnectivities between the different causes of conflicts in PPP agreements. To this end, the aim of this paper is to fill the gap in knowledge by providing a deeper understanding of the causalities or relationships between the different factors that cause disputes in PPP projects in the United States. The authors used a comprehensive analytical approach that involved three primary steps. First, 37 PPP case studies of infrastructure and construction projects were collected and analyzed using manual content analysis. Second, social network analysis was conducted to study the interdependencies between the different causal factors leading to disputes in PPP in general and in relation to Execution, Investment and Operation, and Third-Party Claims, in particular. Third, association rule analysis was conducted to identify key associations between the different causal factors that may trigger the three different types of PPP disputes. The findings showed that the key causes of disputes in PPP projects are related to (1) legal and regulatory, (2) payment and financial, and (3) poor management. While Execution-related disputes were found to be caused by complex interactions of causal factors, dispute causation of Investment and Operation–related and Third-Party Claims–related disputes seemed to be less simplistic. As such, the outcomes of this paper highlighted the important aspects required to avoid dispute occurrence in PPP projects. Ultimately, this paper contributes to the body of knowledge by providing directions for scholars and practitioners toward the aspects and interdependencies that require optimization and/or thorough consideration to avoid dispute occurrence and subsequently ensure successful implementation of PPPs. [ABSTRACT FROM AUTHOR]