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This study aims to examine the challenges facing the construction industry practitioners toward adopting digital construction technologies in the Jordanian construction industry.

Quantitative methods were used by reviewing the related literature to include 16 challenges that face the Jordanian construction industry in adopting digital construction. A questionnaire was used to achieve the desired study objectives for 373 respondents from various institutions and companies. The questionnaire was analyzed with SPSS using statistical tests such as mean score, Kruskal–Wallis H test and factor analysis.

After collecting the quantitative data, the study showed that the most challenges facing construction industry practitioners toward adopting digital construction techniques are lack of qualified workers, high requirement for computing equipment’s, high initial cost of bringing these technologies to the market and construction firms low investment in research and development. These challenges faced by respondents were divided into three main factors, namely, construction’s nature, financial constraints and poor management support.

This study provides information and statistics on the challenges that face individuals or companies toward adopting digital construction techniques in Jordan. It proposes recommendations and proper practical implantation strategies to overcome the challenges.

The purpose of this paper is to develop new predictive models using gene expression programming in order to estimate the compressive strength of green concrete, as accurate models that can predict the compressive strength of green concrete are still lacking.

To estimate the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, four predictive GEP models are developed. The GEP models are developed using a large and reliable database that is collected from the literature. The GEP models are validated using the collected experimental database.

The R² is used to statistically evaluate the performance of the GEP models wherein the R² values for the GEP models including all data are 85, 95, 80 and 95.3 percent for the models that predict the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, respectively.

The GEP models have high R² values and low RMSE and MAE, which indicates that they are capable of predicting the compressive strength of green concrete with a reasonable accuracy.

The purpose of this study is to develop two empirical models that predict the shear strength of exterior beam-column joints exposed to monotonic and cyclic loading using Gene expression programming (GEP).

The GEP model developed for the monotonic loading case is trained and validated using 81 data test points and that for cyclic loading case is trained and validated using 159 data test points that collected from different 9 and 39 experimental programs, respectively. The parameters that are selected to develop the cyclic GEP model are concrete compressive strength, joint aspect ratio, column axial load and joint transverse reinforcement. The monotonic GEP model is developed using concrete compressive strength, column depth, joint width and column axial load.

GEP models are proposed in this paper to predict the joint shear strength of beam-column joints under cyclic and monotonic loading. The predicted results obtained using the GEP models are compared to those calculated using the ACI-352 code formulations. A sensitivity analysis is also performed to further validate the GEP models.

The proposed GEP models provide an accurate prediction for joint shear strength of beam-column joints under cyclic and monotonic loading that is more fitting to the experimental database than the ACI-352 predictions where the GEP models have higher R² value than the code formulations.