The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure's damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach's effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

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This paper presents a framework of an ad-hoc data analytic and Business Intelligence service tailored to a construction project. Mandates of delivering integrated information solutions and effective reporting are commonly required nowadays in large capital projects. Due to the nature of construction projects with schedule and budget constraints, poorly defined business problems prohibited the team to deploy full scale data analytic and Business Intelligence (BI) services on site. On the other hand, the increasingly complex data coming from multiple applications and organizations on projects requires more powerful data integration tools and techniques. The proposed framework outlines an agile and ad hoc best practice for job site data analytics and effective reporting based on a real use case from a large pharmaceutical project. Processes in the framework include data alignment, Level of Detail (LoD) data articulation and analytical model establishment. It also illustrates how to resolve complex data analytic challenges for unforeseen cost disputes and how to deliver solutions within a short period of time.