Abstract: A computing platform is configured to: (i) train a machine-learning model by carrying out a machine learning process on a training data set that includes that includes construction-based data objects including indications of a plurality of building elements and indications of respective logical relationships between pairs of the building elements, (ii) receive a request to generate a building elements graph for a given construction project, (iii) input construction project data associated with the given construction project into the machine-learning model, thereby (a) identifying building elements of the given construction project, (b) determining a set of respective physical relationships between pairs of the building elements and a given set of respective logical relationships between pairs of the building elements, and (c) based on the given set of respective physical relationships and the given set of respective logical relationships, generating a building elements graph for the given construction project

Abstract: A computing platform is configured to: (i) receive, from a first client station, (a) data related to the first client station and (b) a first image associated with a target location; (ii) based on the received data related to the first client station, determine a location signature associated with the first image; (iii) determine that the location signature associated with the first image has a threshold level of similarity to a location signature associated with a second image that is associated with the target location; (iv) evaluate at least the first image to determine progression of a construction-related activity at the target location; (v) based on the evaluation of at least the first image, determine that the construction-related activity at the target location has progressed a threshold amount; and (vi) transmit, to a second client station, a communication related to progression of the construction-related activity.

Abstract: A computing platform is configured to: (i) train a machine-learning model by carrying out a machine learning process on a training data set that includes historical construction-based data objects, (ii) receive a request to generate a construction-based data object associated with an ongoing construction project, (iii) receive data values for data fields of the construction-based data object, (iv) input one or more data values for data fields of the construction-based data object into the machine-learning model, as the input data values, and thereby generate an updated data value for the data fields, (v) cause a client device to present a visual interface, the visual interface usable for viewing an indication of the construction-based data object and an indication of the updated data value, and (vi) update the data fields of the construction-based data object, based on the updated data value.

Abstract: Techniques for dynamic pull planning involve (i) determining an update to a master schedule for a construction project that comprises tasks having respective scheduled start dates, (ii) executing a machine learning model that has been trained with historical construction project schedule data and thereby identifying candidate tasks each available for commencement earlier than its scheduled start date, (iii) causing a client station to display each identified task, its scheduled start date, a respective new start date, and an impact on the master schedule if the task is commenced on the respective new start date, (iv) receiving user input indicating selection of a given task that is to be commenced earlier than its scheduled start date, (v) and causing transmission of a notification to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requesting approval for the earlier commencement.

Abstract: A computing platform is configured to: (i) train a large language model (LLM) by carrying out a first machine learning process on a first training data set that includes first construction-based data associated with one or more of a user, a plurality of reference construction projects, a construction-based application of the computing platform, or combinations thereof, (ii) receive a request to generate a construction activity summary, which includes a context-based prompt, (iii) generate the construction activity summary by inputting the request into the LLM, the construction activity summary including a contextual response, and (iv) retrain the LLM by carrying out a second machine learning process on a second training data set that includes the first training data set and one or more of the context-based prompt, the construction project data, an evaluation, the contextual response, the construction activity summary, a given timeframe, the request, input, or combinations thereof.

Abstract: A computing platform is configured to: (i) receive, from a first client station, (a) data related to the first client station and (b) a first image associated with a target location; (ii) based on the received data related to the first client station, determine a location signature associated with the first image; (iii) determine that the location signature associated with the first image has a threshold level of similarity to a location signature associated with a second image that is associated with the target location; (iv) evaluate at least the first image to determine progression of a construction-related activity at the target location; (v) based on the evaluation of at least the first image, determine that the construction-related activity at the target location has progressed a threshold amount; and (vi) transmit, to a second client station, a communication related to progression of the construction-related activity.

Abstract: A computing system is configured to determine an update to a master schedule for a construction project that comprises a plurality of tasks, each task comprising a respective scheduled start date; execute a machine learning model that has been trained with historical construction project schedule data and thereby identify one or more tasks that is each a candidate for commencement earlier than its scheduled start date; cause a client station to display each identified task, its scheduled start date, a respective new start date, and an impact on the master schedule if the task is commenced on the respective new start date; receive user input indicating a selection of a given task that is to be commenced earlier than its scheduled start date; and cause a notification to be transmitted to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requests approval for earlier commencement.

Abstract: An example computing platform is configured to: obtain a two-dimensional drawing of a portion of a construction project; perform an image processing analysis of the two-dimensional drawing to identify one or more location entities within the two-dimensional drawing; derive embeddings for each location entity in the two-dimensional drawing; based on the derived embeddings, determine relationships between the one or more location entities; and based on the determined relationships between the one or more location entities, generate a location entity data taxonomy that includes each identified location entity as a respective node that is related to at least one other location entity.

Abstract: A computing platform is configured to: (i) receive, from a first client station, (a) data related to the first client station and (b) a first image associated with a target location; (ii) based on the received data related to the first client station, determine a location signature associated with the first image; (iii) determine that the location signature associated with the first image has a threshold level of similarity to a location signature associated with a second image that is associated with the target location; (iv) evaluate at least the first image to determine progression of a construction-related activity at the target location; (v) based on the evaluation of at least the first image, determine that the construction-related activity at the target location has progressed a threshold amount; and (vi) transmit, to a second client station, a communication related to progression of the construction-related activity.

Abstract: A computing platform is configured to (i) receive data objects related to a construction project, (ii) add the data objects to a construction knowledge graph as nodes that are connected to other nodes representing other data objects, (iii) determine, via a machine-learning model trained using historic construction project data, a first risk score for a first data object, (iv) determine, via the machine-learning model, a second risk score for a second data object, where the second risk score is based on (a) the first risk score and (b) a degree of separation between the first data object and the second data object in the construction knowledge graph, (v) based on the second risk score, automatically generate a suggested action to be taken with respect to the first data object, and (vi) cause an indication of the suggested action to be displayed at a client station of a user associated with the construction project.

Abstract: A computing system is configured to determine an update to a master schedule for a construction project that comprises a plurality of tasks, each task comprising a respective scheduled start date; execute a machine learning model that has been trained with historical construction project schedule data and thereby identify one or more tasks that is each a candidate for commencement earlier than its scheduled start date; cause a client station to display each identified task, its scheduled start date, a respective new start date, and an impact on the master schedule if the task is commenced on the respective new start date; receive user input indicating a selection of a given task that is to be commenced earlier than its scheduled start date; and cause a notification to be transmitted to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requests approval for earlier commencement.

Abstract: A method includes receiving, via a user interface, target performance data for a building product configuration. Performance data for each of a plurality of building product configurations is automatically determined. Each of the plurality of building product configurations includes a glass product and a framing system. One or more satisfactory building product configurations are identified from the plurality building product configurations. The performance data of the one or more satisfactory building product configurations satisfies the target performance data. User-selectable configuration options are derived based, at least in part, on the one or more satisfactory building product configurations. Information related to the user-selectable configuration options is published to the user interface. A user selection of a building product configuration from the one or more satisfactory building product configurations is received.