Journal of Management in Engineering

Studies on the international construction market have been limited to expanding the scope of academics and practices because of data accessibility and timeliness. With the recent advancement of natural language processing (NLP) technologies, it becomes possible to extract on-time information from online news articles automatically. As a point of departure for developing a text-based information extraction model, this study aims to develop a named entity recognition (NER) model that automatically detects active players from news articles in the international construction industry. NER is an essential subtask of information extraction that automatically identifies key elements and classifies them into predefined categories. The proposed model detects owners, contractors, and consultants from news articles. The performance of the experiment was measured by a micro average F1 score of 85.8% with precision and recall values of 84.2% and 87.4%, respectively. This study contributes to investigating international market participants in a timely way with enhanced data accessibility. Therefore, the following studies will enlarge the NER approach to recognize “Who contacts whom,” “Who claims whom,” and “What delays what projects,” which will lead to extracting more valuable information automatically in the future.

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’ (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.

Data-driven social equity assessments of infrastructure performance are essential for reducing inequity and for institutionalizing community stakeholder considerations in infrastructure asset management programs. We expand upon literature at the intersection of engineering management and social impact, which has focused on the engineering workforce or the stakeholders of individual projects, to inform equity assessments of overarching programs and their outcomes. We conducted a social equity assessment of the National Bridge Inventory (NBI) component condition ratings by comparing the contribution of established bridge deterioration modeling variables to community demographics surrounding the bridge. To do this, we extend existing ordered probit regression modeling approaches of bridge component condition ratings to a national dataset that spatially matches NBI bridge coordinate data to US Census Bureau tracts to append indicators for income, race, ethnicity, and disadvantaged communities. We found that bridges located in lower-income tracts, tracts identified as disadvantaged communities by the Climate and Economic Screening Tool (CEJST), and tracts with a majority of Black or African American individuals are more likely to be in poor condition. At least on an associative basis, we find that bridge condition is not equitably distributed across communities in the United States, even when controlling for differences in deterioration due to age, traffic type, traffic volume, bridge materials, waterways, and climate. This study suggests that racial equity should be an added component (beyond economic measures of inequity) that informs bridge maintenance program funding and project prioritization to ensure greater equity in our bridge system.

Man-made disasters are often unexpected events that necessitate a comprehensive investigation to ascertain their cause. These investigations are critical for government agencies, insurance corporations, and other stakeholders. Social media platforms are a rich source of information, but the credibility and vast amount of data available make it challenging to extract useful evidence. Therefore, understanding the credibility of information from different groups of users on social media and its role in retrieving and filtering relevant sources is crucial. To illustrate the potential in this regard, this study examines the Notre Dame fire as a case study, analyzing tweets posted between April 15 and 25, 2019. Using a collection of lexicons, the study establishes a text-parsing and lexicon-based rule model to classify users’ opinions regarding the causes of the fire incident. Then the study characterizes the distribution of opinions between verified and nonverified users and investigates the temporal dynamics of reactions from subsets of users commenting on the event. The findings suggest that opinions from verified users were consistent with official reports, which highlights the potential value of the shared knowledge of verified users in the early stages of disaster investigation. The study further suggests that disaster responders should consider opinions from nonverified users, as they may aid investigations by identifying potential causes and providing new directions. In conclusion, this paper explores the potential of utilizing social media data as a means of supporting engineering investigations and emphasizes the importance of developing robust methodologies from crowdsourcing opinions for engineering investigations in the context of man-made disasters.

This paper investigates the effects of two types of government subsidies: the unit cost (UC) subsidy and the floor-area ratio (FAR) subsidy on emissions abatement of a prefabricated construction supply chain (PCSC), comprising a project contractor (PC) and a prefab manufacturer (PM). In our model, the PC subsidized by the government decides a prefabricated ratio (i.e., the ratio of the prefabs to the sum of the prefabs and on-site produced components) and purchases prefabs from the PM for assembly construction, which enables fewer emissions than on-site construction. We find that when assembly construction is less cost-efficient than on-site construction, only the UC subsidy can promote the adoption of prefabricated construction; otherwise, both subsidies are capable of doing so, with the FAR subsidy always inducing a higher prefabricated ratio than the UC subsidy. Furthermore, the UC subsidy holds a direct-pass-through effect, which raises the prefab price, whereas the FAR subsidy stabilizes the prefab price and can induce a win-win situation for the government and the PCSC members under certain circumstances. Regarding emissions abatement, the UC subsidy helps to reduce total emissions and emission intensity, while the FAR subsidy may unexpectedly exacerbate the total emissions but always outperforms the UC subsidy in reducing emission intensity. Finally, we conduct a case study to verify the validity of the proposed model. Our study adds to the body of knowledge by providing quantitative policy optimization for emissions abatement of PCSC.

Abstract

Negotiation is widely known as the most effective way to resolve differences. Traditions of negotiation studies have contributed to this area by providing prescriptive, quantitative, and behavioral research to improve the success of negotiation. This study argues that negotiators’ settlement intentions underpin the functionality of advice under the three traditions of negotiation studies. This study aims to establish ways to identify the intention to settle (ITS) of project dispute negotiators. A longitudinal study was conducted, and a questionnaire was designed by operationalizing an ITS framework that includes technique-based, relationship-based, and cognition-based intention from a thorough literature review. Two rounds of data were collected from the same group of project negotiation practitioners at a three-month interval. The time consistency of the responses was supported by the results of a paired t-test. The same ITS structure was achieved through the test and retest methodology with the two sets of cross-sectional data, supporting the reliability of the structure. The framework was validated to be stable across time by testing the longitudinal factorial invariance. With these steps, a robust ITS identification instrument was developed. The practical applications of the ITS instrument were confirmed by practicing project dispute negotiators. The study theoretically contributes to the body of knowledge by offering a validated ITS framework. On the practical side, the ITS framework is further developed into an identification instrument that can be used in negotiation studies. Notably, the ITS identification instrument can be incorporated into the negotiation decision mechanisms to support negotiators in gauging how conducive in terms of intention to the settlement of a negotiation.

Practical Applications

The primal traditions of negotiation studies (i.e., prescriptive, quantitative, and behavioral) have an implicit premise that negotiators are rational economic men with the intention to resolve disputes. This may not always reflect reality, especially regarding the intention to resolve. To assist negotiating parties in better understanding the prospect of negotiation, this study develops an instrument for the identification of intention to settle (ITS) in project dispute negotiation. A longitudinal study with a mixed-methods approach is used to identify the negotiator’s ITS. The finding supports that negotiators’ settlement intentions are a multidimensional construct that can be manifested as technique-based, relationship-based, and cognition-based intention. The work contributes in three respects: (1) theoretically, the robust ITS framework complements and underpins the functionality of prior negotiation studies; (2) in practice, the ITS identification instrument can assist negotiation decisions, especially in assessing the readiness to strive for a negotiated settlement; and (3) the test-retest methodology used in this study can be applied to other project management studies to enhance the reliability and validity of the findings.

A public–private partnership (PPP) is a common procurement model implemented worldwide as a catalyst for economic growth and improved public infrastructure. However, due to their inherent characteristics, the risk of failure in some PPP projects is high, causing heavy losses to both entities. Despite distinctive progress being made in PPP projects to reduce their failure probability, there is no proper and effective framework to predict PPP project failure in advance in either developing or in developed countries. The present study aims to develop a machine learning (ML) model to predict the failure of PPP projects to prosper in adverse conditions. This research addresses two critical issues, i.e., class imbalance and interpretability of ML models, that differentiate the current study from data-driven studies to date. First, existing studies usually focused on comparing and selecting the most adequate ML methods, but this study distinctively compared the performances of nine data resampling algorithms. Besides, in order to enhance the interpretability and visibility of the proposed model, a game theory–based feature investigation algorithm, Shapley additive explanations (SHAP), was used to identify not only the most significant features, but also the conditions of the features that cause failure or success in PPP projects. The findings illustrate that the proposed model yielded the highest prediction performance once the data set was resampled with the support vector machine-synthetic minority oversampling technique (SVM-SMOTE). SHAP analysis further shows that unsolicited proposals, domestic credit to the private sector, and project type/subtype have significant impacts on the prediction rationale. Overall, this study contributes to theory through incorporating resampling methods and SHAP algorithm into ML models as well as to practice with an advanced and reliable model to predict the status of PPP projects. The data-driven model and findings are expected to respond to current policy and industry needs by proposing a robust decision-making input for detecting risky PPP projects, allocating resources more effectively based on the most critical failure factors, and promoting the transparency of PPP project outcomes.

Hydropower project construction faces huge complexities and uncertainties. Construction claims are very likely to occur, creating losses to contractors and resulting in damage at project level such as breakdown in multiparty cooperation and delays in project completion. Previous studies tended to use subjective methods such as interviews and questionnaires to collect data for claim management research, and did not explore how causal factors interact and evolve to claims. This study aims to explore relationships among causal factors of construction claims (CFCCs), identify critical CFCCs, reveal direct and fundamental causes of claims, and tailor strategies to improve claim management. This work was based on 192 project cases collected from field investigations and academic and official sources; 58 CFCCs and 140 claim causation chains were extracted, forming CFCC networks. CFCCs (nodes) with high total degree and intermediary centrality values in the networks, such as schedule delay, cost increase, and increase of quantities were most critical. Preventing these factors will help eliminate claims. CFCCs with high out-degree values, such as client scope change, changes in construction conditions, construction interruption, and natural hazards, will cause more CFCCs and should therefore be particularly controlled. Besides, CFCCs with high clustering coefficients, such as influence of weather, design errors or omissions, inconsistency between drawings and bill of quantities, construction without advance payment from client, and insufficient site reconnaissance, need to be properly handled because their neighboring nodes can easily influence each other. It was also found that most CFCCs related to engineering influence would directly cause construction claims, and CFCCs pertaining to contractors’ behaviors and external risks tended to fundamentally contribute to construction claims in hydropower projects. The findings and management strategies of this study provide references for claims managers in practice, and are valuable for academics exploring claim management mechanisms in other types of projects.

In the architecture, engineering, and construction (AEC) industry, manual compliance checking is labor-intensive, time-consuming, expensive, and error-prone. Automated compliance checking (ACC) has been extensively studied in the past 50 years to improve the productivity and accuracy of the compliance checking process. While numerous ACC systems have been proposed, these systems can only deal with requirements that include quantitative metrics or specified properties. This leaves the remaining 53% of building requirements to be checked manually, mainly due to the ambiguity embedded in them. In the literature, little is known about the ambiguity of building requirements, which impedes their accurate interpretation and automated checking. This research thus aims to address this issue and establish a taxonomy of ambiguity. Building requirements in health building notes (HBNs) are analyzed using an inductive approach. The results show that some ambiguous clauses in building requirements reflect regulators’ intention while others are unintentional, resulting from the use of language, tacit knowledge, and ACC-specific reasons. This research is valuable for compliance-checking researchers and practitioners because it unpacks ambiguity in building requirements, laying a solid foundation for addressing ambiguity appropriately.

The significance of environmental management of existing buildings in reducing the environmental impact of the construction sector is increasingly emphasized. However, life-cycle assessment (LCA) to evaluate a building’s environmental burden requires considerable time and costs for the collection and interpretation of data needed for analysis. Therefore, this study proposed an integrated assessment framework of economic, environmental, and human health impacts using scan-to-building information modeling (BIM) and LCA in existing buildings. To verify the proposed framework, a case study was conducted. A BIM model with an error rate of 1.442% can be automatically generated from the three-dimensional (3D) point cloud using parametric algorithm. The total cost for the target case was calculated at USD 162,769, and the environmental cost accounted for the largest percentage at 97.15%. The developed framework in this study holds significance because it enables the automated assessment of economic, environmental, and human health impacts of existing buildings through the integration of BIM and LCA.

Effective crane management is imperative to ensure that construction projects are completed on time, within budget, and with a high degree of safety. Prior research on crane management has mostly focused on satisfaction of spatial constraints and minimization of overlapping areas for operation of multiple cranes. However, little attention has been paid to maximizing concurrent crane operations that justify interdependencies among paths, cranes, overlapping areas, and tasks. To overcome this limitation, this study aims to improve multicrane layout planning comprehensively by accounting for various crane operational and managerial parameters (testing various crane specifications and layouts along with alternative cranes, motion paths, and task execution queues) and their parametric interdependencies. Results of the study on an actual construction site demonstrated the improved ability of the developed model in terms of efficiency compared with existing approaches. Therefore, this research anticipates assisting project managers and practitioners with a model for efficient decision making regarding site layout plans for operation of multiple tower cranes.

Abstract

Rapid growth in the Chinese construction industry is exposing many design professionals to elevated stress levels with negative implications for their health and well-being, and in-turn project outcomes. However, it is surprising that there has been little research into job stressors affecting design professionals in the construction industry. The aim of this paper is to help address this gap in knowledge by reporting the results of research that explored the job stressors of design professionals and how they vary across various personal and workplace characteristics. Data was collected via a survey of 408 design professionals in the Chinese construction industry, supported by in-depth sensemaking interviews with nine Chinese design professionals. Survey data was analyzed using both descriptive and inferential statistics combined with qualitative analysis of interview transcripts. Results indicate that task-related stressors are the leading sources of job stress for design professionals in the industry. Managing job workload, job requirements, and job ambiguity are the main task-related stressors and are linked to time pressures, working overtime, high workload, balancing multiple projects, and high levels of responsibility. Results also indicate that gender, marital status, age, education background, years of design experience, design discipline, position, and professional title are mediating factors in a design professional’s job stressor profile. Being the first large-scale empirical investigation of the job stressors among design professionals in the Chinese construction industry, the findings provide new empirical and conceptual insights into an increasingly important yet under researched problem and concludes with practical recommendations to reduce job stress for design professionals in the industry, which has broader relevance in other countries.

Practical Applications

The current research findings have important practical implications for Chinese design professionals and for managers of design organizations and construction projects in China that involve design professionals. By providing new insights into the critical stressors that affect design professionals and the influence of personal and workplace characteristics on their job stressors, this research provides a strong empirical foundation for new evidence-based stress management initiatives and policies to reduce job stressors in this critically important professional group. For example, this may include involving design professionals or design organizations in the redesign of their jobs and working environments and the development of flexibility policies that enable better work-life balance, such as working remotely, paternity leave, and shorter work weeks. More importantly, the findings point to the importance of changing the demanding nature of work in the construction industry and developing more targeted measures to improve individual resilience and stress-coping capabilities for design professionals, especially those subgroups to which the results indicate are subject to stronger job stressors. These groups include electrical design professionals, design professionals with a higher level of education or job position, and professionals with family caring responsibilities.