There are various modeling techniques adopted for predicting the labor productivity that incorporate the influence of various factors, but neural networks are found to have strong pattern recognition and higher accuracy to get reliable estimates. To develop a construction labor productivity (CLP) model for concreting activities, data were collected through a questionnaire. The most critical influencing parameters were ranked according to their relative importance index (RII). Then the strength of the relationship between CLP and influencing parameters was analyzed using correlation coefficient results generated in Python program. Finally, the CLP model which represents the output of the crew within a certain factor was successfully developed using artificial neural networks (ANNs). Five objectives and six subjective critical influencing parameters were selected using RII. Crew experience, age of workers, and placement technique are the top three influencing parameters that have a strong relation with CLP with correlation coefficient values of 0.5681, 0.5349, and 0.5227 respectively. The developed model within these influencing parameters has a higher capability to predict the output of labor with a 92% coefficient of determination (R2) and a mean squared error of 0.316%. Therefore, the application of such a model for the accurate estimation of labor productivity is recommended.

Non-destructive testing has many advantages, such as the ability to obtain a large number of data without destroying existing structures. However, the reliability of the estimation accuracy and the limited range of applicable targets remains an issue. This study proposes to a novel pin penetration test method to determine the early age compressive strength of concrete before demolding. The timing of demolding and initial curing are determined according to the strength development of concrete. Therefore, it is important to determine the compressive strength at the early age before demolding at the actual construction site. The applicability of this strength estimation methodology at actual construction is investigated. Small test holes (12 mm in diameter) are prepared on the mold surface in real construction sites and mock-up specimens in advance. The pin is penetrated into these test holes to obtain the relationship between the compressive strength and the penetration depth. As a result, it is confirmed that the pin penetration test method is suitable to measure the early age compressive strength at actual construction site. This allows the benchmark values for compressive strength, necessary to avoid early frost damage, to be directly verified on the concrete structural members at the construction site. For instance, the compressive strengths of greater than 5 MPa and 10 MPa can be confirmed by the penetration depths benchmark values of 8.0 mm and 6.7mm or less, respectively.