The ability to hear auditory safety cues of mobile equipment while wearing hearing protection equipment (HPE) is critical to preventing injuries and deaths in construction. Existing collision hazard detection models using proximity technologies have limited applicability due to the need for an expensive and complex deployment of sensing devices on every piece of construction equipment. This study proposes a more affordable collision prevention technology that uses audio signals to detect the presence of mobile equipment. The study addresses the problem by improving the auditory situational awareness for construction workers exposed to loud noises with a novel sound detection model that uses artificial intelligence (AI) to detect the sound of collision hazards buried in a great deal of ambient noises. This study included three phases: (1) collecting audio data of construction equipment, (2) developing a novel audio-based machine learning model for automated detection of collision hazards, and (3) conducting field experiments to investigate the system’s efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

Safety-critical sounds at job sites play an essential role in construction safety, but hearing capability is often declined due to the use of hearing protection and the complicated nature of construction noise. Thus, preserving or augmenting the auditory situational awareness of construction workers has become a critical need. To enable further advances in this area, it is necessary to synthesize the state-of-the-art auditory signal processing techniques and their implications for auditory situational awareness (ASA) and to identify future research needs. This paper presents a critical review of recent publications on acoustic signal processing techniques and suggests research gaps that merit further research for fully embracing construction workers’ ASA of hazardous situations in construction. The results from the content analysis show that research on ASA in the context of construction safety is still in its early stage, with inadequate AI-based sound sensing methods available. Little research has been undertaken to augment individual construction workers in recognizing important signals that may be blocked or mixed with complex ambient noise. Further research on auditory situational awareness technology is needed to support detecting and separating important acoustic safety cues from complex ambient sounds. More work is also needed to incorporate context information into sound-based hazard detection and to investigate human factors affecting the collaboration between workers and AI assistants in sensing the safety cues of hazards.

One of the most crucial tasks that a project owner has to undertake is choosing the most competent contractor for the project. The current body of knowledge on contractor prequalification and selection through bidding focuses on the development of multiple-criteria models using decision makers’ subjective judgments. Few studies have used existing data from past projects. This study presents a data-driven approach to evaluating contractors’ production performance on past highway projects, a critical but rarely considered aspect of current evaluation systems. Using historical daily work report data, actual production rates were calculated for controlling activities in past projects. These rates were classified into three tiers of contractor production performance; four contractor-independent factors, which influence rates, location, project budget, weather, and quantity of work, were considered by applying classification techniques, distribution fitting, and Monte Carlo simulation. Performance indexes were proposed in order to enable comparisons. The proposed framework allows project owners to evaluate contractors more objectively and comprehensively by considering an additional but critical criterion in the existing evaluation process, that is, contractors’ past production performance.

Scope of work requirements (SOWRs) specify the contractual obligations for the contractor to fulfill. Managing the information is crucial to the success of a construction project. This process is, however, often challenging because requirements are typically voluminous and written in an unstructured paper-based format. The current state-of-the-art models are mostly applicable to quantitative constraints for use in automated code compliance checking of design features. This study proposes a novel natural language processing (NLP) framework capable of digitalizing nonquantitative natural language SOWRs outlined in construction contracts. The model includes a comprehensive set of semantic and syntactic rules of linguistic features for extracting contractual work information including actors, actions, objects, constraints, tasks, and obligation. The framework was evaluated on a large textual corpus of provisions of design-build highway contracts. The model yields an impressive precision and recall of more than 93% and 87%, respectively. The proposed system is expected to help the project planner quickly develop an electronic database of contractual work requirements, enabling significantly improved efficiency in project planning and verification.

Construction projects are key sources of data changes made to a transportation asset management system (TAMS). Because projects create new assets or modify existing ones, corresponding edits are required to ensure that the database reflects the actual status (e.g., geolocation, geometry) of the infrastructure system. In the current practice, due to the lack of an effective handover mechanism to allow for a direct communication between project and asset management, recording asset changes are significantly laborious and highway agencies encounter various data loss issues. This study aims to develop a lifecycle data model that can guide project handover and allow for project deliverables to become mergeable into TAMSs. The study conducted a large number of focus group discussions and interviews with highway professionals over a 1-year period to capture the flow of asset data throughout the lifecycle of various types of transportation assets. This information was used to develop a handover data model to support transferring design and construction data to asset management. The model was tested on a case study in which the abstract model was compiled into an ontology in the Ontology Web Language (OWL) format. The data of a real traffic sign project were modeled as instances in the OWL ontology. The experiment results indicated that reasoning rules and queries based on the proposed model can successfully extract required asset data from project documents.