Design information extraction from construction specifications to support cost estimation

Construction cost estimation is a labor-intensive task that involves several processes. Although some of these processes have been automated, construction cost estimation still relies heavily on manual inputs. To compute a cost estimate, an estimator needs to: (1) take off quantities and extract some required cost information from the architectural model or drawing; (2) extract other required cost information from the construction specifications; (3) assign building elements to work or cost items; and (4) retrieve the unit cost of the work or cost items to further compute the cost estimate. To achieve full automation of construction cost estimation, the manual inputs required to classify building elements, and to retrieve pricing information of work items need to be automated. To address that, the authors proposed a new method that uses semantic modeling and natural language processing techniques in developing algorithms that automate the manual processes involved in: (1) extracting design information from construction specifications; (2) using the extracted information to match specified material in the construction specifications with items from an established database; and (3) retrieving the pricing information of the materials specified in the construction specifications. To test the validity of the authors' proposed method, an experiment was conducted using eight wood construction projects in Detroit, MI. The proposed method was utilized to develop an algorithm that can process the construction specifications automatically and retrieve the unit cost of materials from a database. The results from the developed algorithm were compared with the gold standard (results manually generated by industry experts). The developed algorithms achieved 99.2% precision and 99.2% recall (i.e., 99.2% F1-measure) for extracted design information instances; 100% precision and 96.5% recall (i.e., 98.2% F1-measure) for extracted materials from the database. The authors demonstrated that as the training data increases, the performance levels increase. The developed algorithms utilized 5.56% of the time it took using the current traditional method of extracting design information from construction specifications manually. These results showed that the proposed method is promising in developing algorithms that automate the processing of construction specifications to extract the design related information in fulfilling essential information requirements of detailed wood construction cost estimation and in retrieving the unit costs.

Automated summary

Construction cost estimation is a labor-intensive task that heavily relies on manual inputs, despite some processes being automated. The proposed method using semantic modeling and natural language processing aims to automate the extraction of design information and matching materials from database, showing promising results in accuracy and efficiency.