Abstract: A computing system is configured to: (i) create a data science model that is configured to (a) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (b) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project and, (ii) after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project by inputting a given value for the metric into the data science model and thereby causing the data science model to (a) evaluate the given value of the metric, and (b) based on the evaluation, output the prediction of performance on the given construction project.

Abstract: Based on receiving data defining a new data item for a construction project corresponding to a particular category of data items, a computing system (1) automatically: (i) predicts that a change event for the construction project is needed by inputting the new data item into a first machine learning model trained to predict a need for a change event from data items corresponding to certain categories of data items, including the particular category of the new data item, (ii) determines initial recommended data for the predicted change event, and (iii) determines additional data for the predicted change event corresponding to a particular class of additional data by inputting the initial recommended data for the predicted change event into a second machine learning model trained to predict one or more classes of additional data for a change event, and (2) automatically create a data item representing the predicted change event.

Abstract: A computing system is configured to: (i) apply a machine-learning process to a training dataset to train a machine-learning model that is configured to (a) receive a first set of metric-level input values for a construction project of interest and a respective set of metric-level input values for each of a universe of reference construction projects, and (b) output a prediction of a party's performance on the construction project of interest and (ii) utilizing the machine-learning model to produce a prediction of a given party's performance on a given construction project of interest by inputting first and respective sets of metric-level input values into the machine-learning model and thereby causing the machine-learning model to (i) evaluate the sets of metric-level input values, and (ii) based on the evaluation of the sets of metric-level input values, output a prediction of the given party's performance on the given construction project of interest.

Abstract: A computing system is configured to: (i) create a data science model that is configured to (a) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (b) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project and, (ii) after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project by inputting a given value for the metric into the data science model and thereby causing the data science model to (a) evaluate the given value of the metric, and (b) based on the evaluation, output the prediction of performance on the given construction project.

Abstract: An example computing platform is configured to: (i) detect a trigger event for determining a value of a given project attribute for a given construction project having a stored set of project attribute data; (ii) in response to detecting the trigger event, execute an attribute-specific set of one or more predictive analytics pipelines for predicting one or more values of the given project attribute based on respective sets of source data for the one or more predictive analytics pipelines; and (iii) update the stored set of project attribute data for the given construction project based on the one or more values of the given project attribute that are predicted for the given construction project.

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: (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: A computing platform is configured to: (i) obtain a set of data objects related to construction projects, (ii) evaluate the set to identify a substitution-request-specific subset, wherein each data object of the subset corresponds to a respective substitution request, (iii) for each data object of the subset, evaluate the data object to identify one or more topics associated with the data object, (iv) based on the one or more topics, determine a supply-related issues, (v) identify a given construction project that has a likelihood of being impacted by one or more of the supply-related issues, (vi) determine one or more insights for the given construction project based on the one or more supply-related issues, and (vii) transmit, to a client station, data defining the one or more insights and thereby cause an indication of the one or more insights to be presented at a user interface of the client station.

Abstract: A computing platform is configured to: (i) at a first time, input data values for a first set of data variables associated with a given construction project into a first machine-learning model that functions to output a prediction of a first set of reference projects that are similar to the given construction project, (ii) based on historical data for the first set of reference projects, determine a predicted value for a parameter of the given construction project, (iii) at a second time, input data values for a second set of data variables associated with the given construction project into a second machine-learning model that functions to output a prediction of a second set of reference projects that are similar to the given construction project, and (iv) based on historical data for the second set of reference projects, determine an updated predicted value for the parameter of the given construction project.

Abstract: A computing platform is configured to: (i) at a first time, input data values for a first set of data variables associated with a given construction project into a first machine-learning model that functions to output a prediction of a first set of reference projects that are similar to the given construction project, (ii) based on historical data for the first set of reference projects, determine a predicted value for a parameter of the given construction project, (iii) at a second time, input data values for a second set of data variables associated with the given construction project into a second machine-learning model that functions to output a prediction of a second set of reference projects that are similar to the given construction project, and (iv) based on historical data for the second set of reference projects, determine an updated predicted value for the parameter of the given construction project.

Abstract: A computing platform is configured to: obtain a set of data objects representing construction-project-related action items; evaluate the obtained set of data objects and thereby identify two or more budget-code-specific subsets of data objects, where each respective budget-code-specific subset of data objects corresponds to a respective one of two or more budget codes; for each respective one of the two or more budget codes, evaluate the respective budget-code-specific subset of data objects and thereby identify one or more budget-code-specific metrics for the respective one of the two or more budget codes; based at least on the identified budget-code-specific metrics for the two or more budget codes, determine one or more construction-related insights; and transmit, to a client station, data defining the one or more construction-related insights and thereby cause an indication of the one or more construction-related insights to be presented at a user interface of the client station.

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: 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: (i) at a first time, input data values for a first set of data variables associated with a given construction project into a first machine-learning model that functions to output a prediction of a first set of reference projects that are similar to the given construction project, (ii) based on historical data for the first set of reference projects, determine a predicted value for a parameter of the given construction project, (iii) at a second time, input data values for a second set of data variables associated with the given construction project into a second machine-learning model that functions to output a prediction of a second set of reference projects that are similar to the given construction project, and (iv) based on historical data for the second set of reference projects, determine an updated predicted value for the parameter of the given construction project.

Abstract: A computing system is configured to receive a request to create a new change event for a construction project; obtain a set of initial information about the new change event; evaluate the set of initial information using predictive analytics and thereby predict one or more scopes of work that are likely implicated by the new change event, wherein each of the one or more scopes of work comprises a category of work activity and an estimated cost of performing the work activity; cause a client station to present the one or more scopes of work that are likely implicated by the new change event; receive data indicating that the user has selected at least one given scope of work from the one or more scopes of work; and create a data item that represents the new change event and includes data defining the at least one given scope of work.