Hani Nassif

Concrete artificial reef and living shoreline construction has gained popularity in recent years due to their ability to reduce wave energy and facilitate protective coastal habitats. Typically, portland cement based concrete is used to produce artificial reef components. While portland cement concrete has excellent strength and durability properties, its usage can raise sustainability concerns regarding its carbon footprint. Additionally, portland cement based concrete is known for its highly alkaline leachate, which has been cited as a potential factor limiting sessile organism settlement. In this study, an economically viable low carbon self-consolidating concrete is developed by examining the effect of powder constituents such as silica fume, slag, and limestone powder on concrete performance, carbon footprint, material cost, pH of leachate, and laboratory and field testing on oyster larval settlement and growth. Formulation development was optimized for carbon reduction while maintaining performance for usage for durable reef and marine structures.

Truck load spectra based on weigh-in-motion (WIM) measurements have been utilized in developing live-load models for short and medium-span bridges. The current AASHTO load-and-resistance factor design bridge design (LRFD) specifications were initially calibrated for spans up to 200 ft at the strength limit states. Consequently, the necessity to extend and validate the application of the AASHTO LRFD and load-and-resistance factor rating provisions for long-span bridges becomes evident. This paper presents a framework to establish live-load models for long-span bridges based on WIM data. Multiple years of data from twelve WIM sites are utilized to develop the load spectra. A total of seven types of load effects are considered to satisfy load demands. Parameters that influence the live loads for long-span bridges are identified. The current HL-93 nominal load in AASHTO LRFD results in non-uniform mean maximum load across different loaded lengths; therefore, a length factor for the HL-93 design lane load is proposed to achieve uniform live-load factors. Traffic state (i.e., free-flow or congestion) is found to have a significant impact on the live load for long-span bridges. As the results show that the current AASHTO LRFD load only represents free-flow traffic, a congestion factor is proposed. Additionally, permit vehicles are found to have a significant impact on the live loads under free-flow traffic, but their effect is limited for congested traffic. Statistical live-load models are summarized for reliability calibration.

Truck load spectra based on weigh-in-motion (WIM) measurements have been utilized in developing site-specific live load models to predict the maximum load effects on bridges. Conventional load extrapolation has been utilized to develop the AASHTO load-and-resistance factor design (LRFD) Bridge Design Specifications, while few studies have evaluated the accuracy of the load extrapolation techniques with actual data. The current AASHTO Manual for Bridge Evaluation (MBE) utilizes the top 5% of the load effects to extrapolate the 5-year maximum load effects for load rating. However, in the cases of high truck volume, the predicted 5-year maximum load effects using AASHTO MBE are significantly lower than the observed value because of the selection of the upper tail. Therefore, the choice of the upper tail size needs further validation. This paper proposes a modification to the conventional live load extrapolation method. Firstly, more accurate maximum load values for different return periods are determined through simulation and validated using 7 years of continuous data. Then, the values from conventional live load extrapolations using different upper tail sizes are obtained and compared with the simulation values. The optimal upper tail size is determined when the minimum error is yielded. The findings suggest that using a specific number of trucks for the upper tail yields greater accuracy compared to a percentage-based approach. Specifically, the recommended range is between 3,000 to 5,000 trucks, with an optimal number of 3,600. This paper concludes with recommendations to the AASHTO MBE to enhance the accuracy of live load extrapolation.

Highway agencies prioritize maintaining bridge infrastructure through bridge management systems amid budget constraints. The premature deterioration of reinforced concrete (RC) bridge decks caused by more frequent and increasingly heavy truck load spectra coupled with aggressive environmental conditions has become a critical concern. Despite the prevalence of conventional models and the emerging popularity of machine learning (ML) models in bridge deterioration predictions, they fall short in feature selection and handling of climate conditions, leading to suboptimal accuracy. To address these gaps, this study presents a data-driven framework utilizing ML-based techniques to predict the condition rating of RC bridge decks with a focus on identifying the influencing factors that affect the deck condition. The framework employs the XGBoost algorithm for model development, encompassing comprehensive datasets that include structural, geographical, and climate variables from across the U.S. Furthermore, the Shapley additive explanations approach is applied to identify the explanatory variables with the most impact. Age emerged as the most crucial factor, followed by freeze-thaw cycles and truck traffic, as indicated by the average daily truck traffic. Rainfall also plays a substantial role in deck deterioration. Based on feature importance and monotonicity, this study recommends a series of bridge classifications for transportation agencies to incorporate into their deterioration models. Overall, this research enhances understanding of the primary causes of bridge deck deterioration, enabling more informed decisions about funding allocation and bolstering bridge performance against environmental challenges.

The critical live loads in bridge design or evaluation could be caused by either a single vehicle event or simultaneous occurrence of two or more vehicles, which is usually called a “multiple presence event.” The multiple presence factors (MPFs) are then defined to assess the live load effects from single and multiple presence events. The MPFs from the American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) were derived three decades ago, by assuming the probability of multiple presence events based on field observation. In addition, the correlation among truck loads in multiple presence events needs further evaluation based on actual data, as previous studies have assumed either fully correlated or independence across the lanes. There is an imperative need to re-evaluate the MPFs based on the available weight-in-motion data. This paper derives the MPFs for bridge live loads based on both traffic simulation and probabilistic modeling, utilizing existing weigh-in-motion data. It is found that the MPFs are affected by two major factors: truck loads and probability of multiple presence events. Truck loads can be further defined as the mean and standard deviation of loads on each lane, and the covariance of the load effects across the lanes. The probability of multiple presence events is related to average daily truck traffic, and the distribution of the traffic. The dispersion of truck volume and covariance of load effects across the lanes shows significant impact on the MPFs. Statistics of MPFs are obtained and recommendations to AASHTO LRFD are provided for 2-lane and 3-lane MPFs.