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This study examined the structural reliability of the Nigerian grown Opepe timber as bridge beam material. The strength of a particular specie of timber depends so much on some factors such as soil and environment in which it is grown. The steps involved are collection of the Opepe timber samples, seasoning/preparation of the test specimens, determination of the strength properties/statistical analysis, development of a computer programme in FORTRAN language and finally structural reliability analysis using FORM 5 software. The result revealed that the Nigerian grown Opepe is a reliable and durable structural bridge beam material for span of 5000mm, depth of 400mm, breadth of 250mm and end bearing length of 150mm. The probabilities of failure in bending parallel to the grain, compression perpendicular to the grain, shear parallel to the grain and deflection are 1.61 x 10-7, 1.43 x 10-8, 1.93 x 10-4 and 1.51 x 10-15 respectively. The paper recommends establishment of Opepe plantation in various Local Government Areas in Nigeria for structural applications such as in bridges, railway sleepers, generation of income to the nation as well as creating employment for the numerous unemployed youths.

1 Introduction to Reliability and Risk Analysis -- 1.1 Historical perspective -- 1.2 Objectives of reliability and risk analyses -- 1.3 Subject areas. Modelling -- 1.4 Basic concepts -- 1.5 Planning, execution and use of reliability analysis -- 1.6 Planning, execution and use of risk analysis -- 1.7 Discussion -- 1.8 Problems -- 2 Basic Reliability and Risk Analysis Methods -- 2.1 Use of statistics -- 2.2 Failure mode and effect analysis -- 2.3 Fault tree analysis -- 2.4 Cause consequence analysis/Event tree analysis -- 2.5 Other reliability and risk analysis methods -- 2.6 Problems -- 3 Binary Models for Analysing System Reliability -- 3.1 Structural properties -- 3.2 Computing system reliability -- 3.3 Non-repairable systems -- 3.4 Repairable systems -- 3.5 Reliability importance measures -- 3.6 Dependent components -- 3.7 Problems -- 4 Multistate Models for Analysing System Reliability -- 4.1 Multistate monotone systems -- 4.2 Computing system reliability -- 4.3 A time dependent model -- 4.4 Discussion -- 4.5 Problems -- 5 Optimal Replacement Policies -- 5.1 Introduction -- 5.2 General minimal repair, age replacement model -- 5.3 Block replacement model -- 5.4 On-condition replacement models -- 5.5 Problems -- 6 Safety Management -- 6.1 Introduction -- 6.2 Safety management in a company -- 6.3 Reliability management of a product -- 6.4 Problems -- 7 Some Applications -- 7.1 Risk analysis of an offshore oil and gas producing platform -- 7.2 Availability and life cycle costing in a gas project -- 7.3 Availability and life cycle costing related to a process instrumentation system -- 7.4 Discussion -- 7.5 Problems -- Appendicies -- A Probability Theory -- A.l Probabilities -- A.1.1 Probability concepts -- A.1.2 Rules for combining probabilities -- A.1.3 Conditional probabilities -- A.1.4 Independence -- A.2 Stochastic variables -- A.3 Some proofs -- A.3.1 Proof of formula (A.4) -- A.3.2 Probability calculations in event trees -- A.3.3 Proof of an error bound for the approximations (4.2) and (4.3). -- A.4 Problems -- B Stochastic Failure Models -- B.1 Non-repairable units -- B.1.1 Basic concepts -- B.1.2 Some common lifetime distributions -- B.2 Repairable units -- B.3 Binomial distribution -- B.4 Gamma function -- B.5 Problems -- C Statistical Analysis of Reliability Data -- C.1 Identification of lifetime distribution, Hazard plotting -- C.2 Estimation of parameters in lifetime distributions -- C.3 Statistical analysis of non-homogeneous Poisson processes -- C.4 Data sources -- C.5 Problems -- D Markov Models -- D.1 Introduction -- D.2 One component. No repair -- D.3 One repairable component -- D.4 Two repairable components -- D.5 Birth and death processes -- D.6 General formulation of a Markov process -- D.7 Problems -- E More on Stochastic Processes -- E.1 Renewal processes -- E.2 Renewal reward processes -- E.3 Regenerative processes -- E.4 Counting processes -- E.5 Proofs of some results in Chapters 3 and 5 -- E.5.1 Section 3.4.1 -- E.5.2 Section 3.4.2 -- E.5.3 Section 5.4 -- F An Algorithm for Calculating System Reliability -- G A Minimization Technique.

The concept of Simulation Based Reliability Assessment (SBRA) for Civil Engineering structures is presented. SBRA uses bounded histograms to represent variable material and geometric properties as well as variable load effects, and makes use of the Monte Carlo technique to perform a probability-based reliability assessment. SBRA represents a departure from the traditional deterministic and semi-probabilistic design procedures applied in codes and as such requires a "re-engineering" of current assessment procedures in accordance with the growing potential of computer and information technology. Three simple examples of how SBRA can be used in Structural Engineering design are presented: 1) the reliability assessment of a steel beam, 2) a truss bar subjected to variable tension and compression, and 3) the pressure on the wall of an elevated water tank due to earthquake load.

This study, based on part of the author's Ph.D. thesis in mass communications at Wisconsin, points out both the need for concern about reliability errors and the ways in which errors can be reduced. Dr. Stempel is now an instructor in journalism at Pennsylvania State University

A steel frame with haunches is designed according to Eurocodes. The frame is exposed to self-weight, snow, and wind actions. Lateral-torsional buckling appears to represent the most critical criterion, which is considered as a basis for the limit state function. In the reliability analysis, the probabilistic models proposed by the Joint Committee for Structural Safety (JCSS) are used for basic variables. The uncertainty model coefficients take into account the inaccuracy of the resistance model for the haunched girder and the inaccuracy of the action effect model. The time invariant reliability analysis is based on Turkstra's rule for combinations of snow and wind actions. The time variant analysis describes snow and wind actions by jump processes with intermittencies. Assuming a 50-year lifetime, the obtained values of the reliability index b vary within the range from 3.95 up to 5.56. The cross-profile IPE 330 designed according to Eurocodes seems to be adequate. It appears that the time invariant reliability analysis based on Turkstra's rule provides considerably lower values of b than those obtained by the time variant analysis.

Exactitude et validité vues par les modèles à équations structurelles. D'après un examen historique de l'évaluation de l'exactitude et la validité on conclut qu'un point de vue trop étroit la validité est utilisé par les chercheurs travaillant dans la tradition de la modélisation par équations structurelle, surtout dans ses applications avec les modèles factoriels classiques et ses développements récents "multi-traits multi-méthodes". La partition de la variance d'erreur dans cette approche entre effets substantifs et effets de méthode, et sa relation avec l'inexactitude et l'invalidité est expliqué ici: l'alternatif simple, l'interprétation des relations parmi un ensemble d'indicateurs, est présenté ensuite. Un exemple empirique est utilisé pour démontrer la dépendance de l'estimation d'exactitude et de validité du contexte théorique dans lequel elle est faite. On conclut, avec Cronbach, que le sens du terme validité doit être rétabli en comprenant tout l'apport des propriété théorétiques assignées à a mesure et que l'on doit désigner en termes d'exactitude de mesure les biais spécifiques engendrés par les effets de méthodes et d'autres variables confondues.

AbstractThe present study is a reliability‐generalization meta‐analysis of 5 commonly used measures of romantic love: Rubin's Loving and Liking scales, the Love Attitudes Scale, the Love Attitudes Scale–Short Form, the Passionate Love Scale, and the Triangular Love Scale. Data from 127 studies representing 38,132 participants provided internal consistency reliability estimates. The studies provide information on the average score reliability across studies and the effect of sample characteristics on score reliability across measures. The reliability of scores from several measures proved to be susceptible to the influence of sample characteristics, most notably, the ethnicity of the sample. The discussion provides recommendations to researchers and focuses on the ramifications of score reliability on love research.