BIM-Based Methodology for the Management of Public Heritage. CASE Study: Algeciras Market Hall

Building Information Modelling (BIM) is modifying the workflow of the construction field, not only in design and construction stages but also for the management of the facilities. Most advances in academics and industry have focussed on the use of BIM for building. However, the possibilities of the use of three-dimensional information models for the construction and management of public works and civil engineering infrastructure projects (known as CIM) are still a matter of concern, being complex though offering a wider number of possibilities when compared with regular building industry. Moreover, the construction process in comparison with its lifespan represent only a small part of the investments for the use of public works. With this background, the possibilities based on BIM for the maintenance and rehabilitation of public heritage (HCIM) can greatly improve traditional management capabilities. Making best use of BIM and digitalisation for the management of public heritage (HCIM) requires creating tools for documentation, registering and data management to permit the adequate information transfer between the actors involved. Such actors may be experts or not and hold or not skills to use BIM tools. This study proposes the creation of a database to support the regular inspection during the lifespan of the infrastructure and connect it with the three-dimensional information model, serving the latter as an information repository of the whole life of the infrastructure. Such data include damage and causes as well as a description of the pathology and this information is referred to each element, showing all the historic measures taken. In addition, quantification and quotation of the repairs needed can be obtained. Lastly, the study has applied this methodology in Algeciras Market Hall, the notorious rationalist building designed by the engineer Eduardo Torroja and built in 1935. The results shown in this study can be of great interest for both researchers and practice, with an adaptation and innovation of the BIM and HCIM possibilities.

International Roughness Index Modeling For Jointed Plain Concrete Pavement Using Artificial Neural Network

Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for the pavements. This study aims to develop IRI models that can successfully estimate the IRI values for Jointed Plain Concrete Pavement (JPCP) considering the M&R history of the pavements using Artificial Neural Networks (ANNs) approach. The study was carried out with the database collected from Long Term Pavement Performance (LTPP) program. The variables used for the ANN model development are initial IRI, pavement age, concrete pavement thickness, equivalent single axle load (ESAL), climatic region (wet-freeze, wet non-freeze, dry-freeze, dry non-freeze), construction number (CN), and several climatological data. After utilizing various ANN model structures, the best performing ANN model resulted in promising statistical measures (i.e. R2 = 0.87). The IRI prediction model can successfully estimate the increase of IRI values with the increase of ESAL value over time. The IRI prediction model can also estimate the decrease of IRI value after maintenance and rehabilitation. The predicted IRI values with good accuracy will help the local and state agencies to prepare for M&R programs for JPCP pavements and allocate a projected budget accordingly.

Roughness Modeling for Composite Pavements using Machine Learning

A large number of paved highway surfaces comprises composite pavements as a result of concrete pavement rehabilitation that uses an asphalt overlay on top of the concrete surface. Annually, billions of dollars are spent on the maintenance and rehabilitation of road networks. Roughness is one of the several indicators of road conditions used to make objective decisions related to road network management. The irregularities in the pavement surface affecting the ride quality of road users can be described by a standard roughness index defined as the International Roughness Index (IRI). Roughness prediction models can identify rehabilitation needs, analyze rehabilitation effects, and estimate future pavement conditions to implement different Maintenance and Rehabilitation (M&R) activities to extend the pavement life cycle and provide a smooth surface for road users. This study intended to develop pavement performance models to predict roughness for asphalt overlay on concrete pavement sections using the Long-Term Performance Pavement (LTPP) program database. Artificial Neural Networks (ANNs) approach was used to develop roughness prediction models. A total of 52 pavement sections with 592 data points were analyzed. Five models were developed, and the best performing model, Model 5 was found with an average square error (ASE) of 0.0023, mean absolute relative error (MARE) of 12.936, and coefficient of determination (R2) of 0.88. Model 5 utilized one output variable (IRIMean) and 14 input variables (i.e., Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation). The ANN model structure utilized for Model 5 was 14-9-1 (14 inputs, 9 hidden nodes, and 1 output). Environmental impacts and traffic repetitions can cause severe damage to the pavement if timely maintenance and rehabilitation are not performed. By considering the effects of the M&R history of the pavement, it is possible to obtain realistic prediction models for future planning. Therefore, the developed ANN roughness performance models in this paper can be used as a prediction tool for IRI values and guide decision-makers to develop a better M&R plan. Local and state agencies can use available historical traffic and climatological data in the developed models to estimate the change in IRI values. Utilizing these prediction models eliminates time-consuming data collection and post-processing, and consequently, a cost reduction. This low-cost tool will improve the condition assessment and effective M&R scheduling.

Review of Factors Controlling Skid Resistance at Tire-Pavement Interface

Road safety is of prime importance for pavement engineers and maintenance authorities. Pavement resistance to skidding of the vehicle has long been recognized as one of the leading parameters governing road safety and driving comfort, especially in wet weather conditions. The knowledge of skid resistance offered by pavement surface is very valuable information for road safety enhancements. Skid resistance is defined as the force developed when a tire that is prevented from rotation slides along the pavement surface. Evaluation of skid resistance over time and estimation of factors influencing it are important for pavement maintenance and rehabilitation planning. This paper presents a state-of-the-art review of various research works carried out for assessing critical parameters like surface texture, tire tread, rain intensity, temperature, loading condition, tire inflation pressure, and pavement type which control skid resistance of asphalt pavement at tire-road interface significantly. First, a brief overview of skid resistance and its importance in asphalt pavement is provided. Then, critical parameters influencing skid resistance are identified and reviewed more elaborately. Furthermore, the key relationship between skid resistance and various controlling parameters is reviewed and presented for a better understanding of skid variation analysis. Finally, a general discussion on skid resistance governing factors, their relative importance in maintaining safety and pavement performance, the complexity involved in computation, and established relationships with skid resistance is briefly summarized.