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: An example computing system is configured to: (i) access a drawing associated with a construction project; (ii) identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing; (iii) for each candidate textual element, (a) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (b) input the respective dataset into a machine-learning model that functions to (1) evaluate the respective dataset and (2) output, based on the evaluation, a respective score indicating a likelihood that the candidate textual element represents the title of the drawing; and (iv) based on the respective scores for the candidate textual elements that are output by the machine-learning model, select one given candidate textual element as the title of the drawing.

Abstract: A computing platform is configured to: for each construction project in a pool of construction projects, (i) obtain a set of data objects related to the construction project; (ii) evaluate the obtained set of data objects related to the construction project and thereby identify two or more theme-specific subsets of data objects, wherein each respective theme-specific subset of data objects corresponds to a respective one of two or more construction-related themes; (iii) for each respective one of the two or more construction-related themes, evaluate the respective theme-specific subset of data objects and thereby identify a respective theme-specific group of one or more construction-related problems that correspond to the respective one of two or more construction-related themes; and (iv) based at least on the theme-specific groups of one or more construction-related problems that respectively correspond to the two or more construction-related themes, generate a project-specific themes dataset for the constru

Abstract: A computing platform is configured to: for each construction project in a pool of construction projects, (i) obtain a set of data objects related to the construction project; (ii) evaluate the obtained set of data objects related to the construction project and thereby identify two or more problem-specific subsets of data objects, wherein each respective problem-specific subset of data objects corresponds to a respective one of two or more construction-related problems; (iii) for each respective one of the two or more construction-related problems, evaluate the respective problem-specific subset of data objects and thereby identify a respective problem-specific group of one or more construction-related themes that correspond to the respective one of two or more construction-related problems; and (iv) based at least on the problem-specific groups of one or more construction-related themes that respectively correspond to the two or more construction-related problems, generate a project-specific themes dataset f

Abstract: An example computing system is configured to: (i) access a drawing associated with a construction project; (ii) identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing; (iii) for each candidate textual element, (a) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (b) input the respective dataset into a machine-learning model that functions to (1) evaluate the respective dataset and (2) output, based on the evaluation, a respective score indicating a likelihood that the candidate textual element represents the title of the drawing; and (iv) based on the respective scores for the candidate textual elements that are output by the machine-learning model, select one given candidate textual element as the title of the drawing.