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Numerous existing formulas predicted the ultimate interfacial bond strength in concrete-filled steel tubes (CFST) between steel tubes and concrete core without investigating the whole response under push-out load. In this research, four models are proposed to predict the interfacial behavior in CFST including the post-peak branch under the push-out loading test based on 157 circular specimens and 105 squared specimens from the literature. Two models (one for circular and one for squared CFST) are developed and calibrated using artificial neural network (ANN) and two models (one for circular and one for squared CFST) are developed based on multivariable regression analysis, analysis of variance (ANOVA). The shape of the specimen (circular or squared), diameter of the tube, thickness of the tube, concrete compressive strength, age at the time of testing, and length of the specimen are the main factors considered. These models are then compared to other existing formulas to verify their capability to better predict the ultimate interfacial bond strength. It is found that the ANN model gives better results for most of the considered data. It is also found that ANN models can predict the overall bond-slip response for the considered dataset. In order to simulate the response of any CFST column using finite element (FE) method, it is vital to have sufficient input data on the overall bond-slip behavior between the interior face of the steel tube and the exterior surface of the concrete core including the post-peak branch. Accordingly, the suggested ANN model is used to generate the required input data related to the cohesive behavior and damage along the interface in ABAQUS model to simulate the response of two circular and two squared CFST columns under concentric compressive load. The results are in good agreement with experimental outcomes. The cohesive criterion and damage interface that are used based on ANN models in FE are found to be sufficient and can be adopted to model CFST columns.

A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with R 2 of 0.9286.

The structure of permeable concrete has been the primary reason for its use in construction. Permeable concrete is composed of water, cement, aggregate, and little-to no-fines resulting in the presence of a significant number of voids. This makes permeable concrete an ideal solution to water accumulation issues as it acts as a drainage system. This study employs a feedforward backpropagation artificial neural network model that combines experimental laboratory data from previous studies with appropriate network architectures and training techniques. The purpose of the analysis is to develop a reliable functional relationship, based on water-cement ratio, aggregate-cement ratio, and density parameters, with which to estimate the compressive strength, porosity, and water permeability of permeable concrete. Multiple linear regression correlations are also established to predict and correlate these inputs and outputs. The two derived methods are then compared and discussed. The results reveal that ANN is better to anticipate the permeable concrete properties than regression analysis.

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Tuned mass dampers (TMDs) as vibration-mitigating devices are widely used in structures to reduce their displacement response under dynamic forces. Through a novel dolphin echolocation (DE) algorithm, this paper provides optimum tuning of TMD parameters. Developing some features of this algorithm results in a faster convergence to the optimum solution. Besides, grey wolf and whale optimization algorithms (GWO and WOA), as two other nature-based meta-heuristic algorithms, are employed in this problem. The modified DE illustrates a more optimum design of TMD's parameters rather than GWO and WOA. The code has been verified by a sample structure from the literature and then applied to a high-rise forty-story structure under strong ground motions. The numerical results reveal that the optimum TMD is viable in attenuating the structural responses, including relative displacements and absolute accelerations under different earthquake excitations. For instance, in the high-rise structure, the modified DE, GWO, and WOA reduce the maximum displacements up to 45%, 43%, and 38%, respectively. Moreover, the algorithms, specifically the modified DE, propose more cost-effective designs in comparison with previous studies in the literature by introducing smaller TMD parameters.

The construction sector in Jordan faces various obstacles, one of the most significant being poor communication, which negatively impacts project performance and can even lead to project failure. The primary objective of this study is to identify and evaluate the causes and effects of poor communication in Jordan. A questionnaire containing 32 causes and 21 effects of poor communication was used to gather data. The top ten causes of poor communication are; possessing different levels of education among construction teams; a lack of communication plan; a lack of an appropriate communications medium; a lack of communication procedures and training; a slow flow of information between parties; diversity of culture among construction teams; construction teams possessing different skill levels; contractual barriers; the unavailability of information at the time of need, and a lack of understanding among parties. Poor communication is a prevalent issue in the Jordanian construction industry, Jordan's construction industry needs to improve its current communication state. Both contractors and consultants are affected by this issue and there is a high level of agreement between them on the causes and effects of poor communication.

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