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Recycled waste glasses have been widely used in Portland cement and concrete as aggregate or supplementary cementitious material. Compressive strength is one of the most important properties of concrete containing waste glasses, providing information about the loading capacity, pozzolanic reaction and porosity of the mixture. This study aims to propose highly nonlinear models to predict the compressive strength of concrete containing finely ground glass particles.

A robust machine leaning method called genetic programming is used the build the compressive strength prediction models. The models are developed using a number of test results on 50-mm mortar cubes containing glass powder according to ASTM C109. Parametric and sensitivity analyses are conducted to evaluate the effect of the predictor variables on the compressive strength. Furthermore, a comparative study is performed to benchmark the proposed models against classical regression models.

The derived design equations accurately characterize the compressive strength of concrete with ground glass fillers and remarkably outperform the regression models. A key feature of the proposed models as compared to the previous studies is that they include the simultaneous effect of various parameters such as glass compositions, size distributions, curing age and isothermal temperatures. Parametric and sensitivity analyses indicate that compressive strength is very sensitive to the curing age, curing temperature and particle surface area.

This study presents accurate machine learning models for the prediction of one of the most important mechanical properties of cementitious mixtures modified by waste glass, i.e. compressive strength. In addition, it provides an insight into the effect of several parameters influencing the compressive strength. From a computing perspective, a robust machine learning technique that overcomes the shortcomings of existing soft computing methods is introduced.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

By designing a system dynamics model in the form of a multimodal transportation system, this study for the first time seeks to reduce costs and time, and increase customer satisfaction by considering uncertainties in the intra city transit system, especially demand uncertainty and provide a prototype system to prove the capability of the dynamical system.

The paper tried to model the factors affecting the intra city multimodal transportation system by defining different scenarios in the cause-and-effect model. The maps and results developed according to system dynamics modeling principles are discussed.

Four scenarios were considered given the factors affecting the urban transportation system to implement the transportation information system for reducing the material and non-material costs of wrong planning of the intra city transit system. After implementing the scenarios, scenario two was selected under the following conditions: advertising for cultural development, support of authorities by efforts such as street widening to reduce traffic, optimize infrastructure, increase and optimize public transport and etc.

The value of this paper is considering uncertainty in traffic optimization; taking into account behavioral and demand indicators such as cultural promotion, official support, early childhood learning, traffic hours and the impact of traveler social status; investigating the factors affecting the system under investigation and the reciprocal effects of these factors and real-world simulation by considering the factors and effects between them.

Gives a bibliographical review of the finite element meshing and remeshing from the theoretical as well as practical points of view. Topics such as adaptive techniques for meshing and remeshing, parallel processing in the finite element modelling, etc. are also included. The bibliography at the end of this paper contains 1,727 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1990 and 2001.

A rapid increase in global smartphone ownership and digital health technologies offers the potential for mobile phone applications (apps) to deliver mental health interventions. The purpose of this paper is to bring together evidence reporting on mental health mobile apps to gain an understanding of the quality of current evidence, the positive and adverse effects of apps and the mechanisms underlying such effects.

A systematic search was carried out across six databases, for any systematic reviews or meta-analyses conducted up to 2020. Review quality was assessed using the Assessment of Multiple Systematic Reviews.

Across a total of 24 articles, a variety of clinical outcomes were assessed. Most compelling support was shown for apps targeting anxiety symptoms; some evidence favoured the use of apps for depression symptoms. Less evidence was available for the remaining clinical symptoms such as bipolar disorder, schizophrenia, post-traumatic stress disorder, sleep disorders and substance use. Overall, there was limited evidence pertaining to adverse effects and change mechanisms and a lack of quality reporting across a large proportion of included reviews. The included reviews demonstrate the need for further robust research before apps are recommended clinically.

This paper makes a valuable contribution to the current status of research and reviews investigating mental health mobile apps. Recommendations are made for improved adherence to review guidelines and to ensure risk of bias is minimised.

Gives a bibliographical review of the error estimates and adaptive finite element methods from the theoretical as well as the application point of view. The bibliography at the end contains 2,177 references to papers, conference proceedings and theses/dissertations dealing with the subjects that were published in 1990‐2000.